Data Science Certification Training

Data Science Certification course in Gurgaon

Enroll now to acquire essential data science principles and elevate your skills as a proficient data science professional. Join today to embark on the journey towards becoming a competent expert in the field of data science.

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40 Hrs

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3 Project

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  • A data science certification course is a structured educational program that provides participants with a comprehensive understanding of the key principles, techniques, and tools used in the field of data science. These courses are designed to equip individuals with the necessary skills to analyze, interpret, and extract meaningful insights from complex datasets. Here are some common components typically covered in a data science certification course:
    • Fundamental Concepts:
      • Participants learn foundational concepts in statistics, mathematics, and computer science, providing the necessary background for data analysis.
    • Data Collection and Cleaning:
      • Understanding the process of collecting and cleaning data is crucial. This involves handling missing values, outliers, and ensuring data quality.
    • Exploratory Data Analysis (EDA):
      • Techniques for exploring and visualizing data to uncover patterns and trends are covered. EDA is an essential step in understanding the structure and characteristics of a dataset.
    • Statistical Analysis:
      • Basic and advanced statistical methods are taught to help participants draw meaningful inferences from data.
    • Machine Learning:
      • Introduction to machine learning algorithms and techniques, including supervised and unsupervised learning, regression, classification, and clustering.
    • Programming Skills:
      • Proficiency in programming languages such as Python or R is often a core component. Participants learn to implement algorithms and manipulate data using these languages.
    • Tools and Technologies:
      • Familiarity with popular data science tools and technologies, such as Jupyter notebooks, pandas, NumPy, scikit-learn, and others, is typically included.
    • Project Work:
      • Many certification courses involve hands-on projects to apply the acquired skills to real-world scenarios. This practical experience enhances the participant's ability to tackle data science challenges in a professional setting.
    • Ethical Considerations:
      • Understanding the ethical implications of working with data, including privacy concerns and responsible data handling, is often emphasized.
  • Upon successful completion of a data science certification course, participants typically receive a certificate that can be recognized by employers as evidence of their expertise in the field. These courses are beneficial for individuals looking to enter the data science profession, as well as professionals seeking to enhance their skills and stay current with industry trends.

  • The course objectives of a data science program generally aim to provide participants with a well-rounded set of skills and knowledge to navigate the complexities of working with data. Specific objectives may vary across programs, but common goals include:
    • Foundational Knowledge:
      • Establish a solid understanding of the fundamental concepts in statistics, mathematics, and computer science that form the basis of data science.
    • Data Exploration and Preprocessing:
      • Teach techniques for collecting, cleaning, and preprocessing data to ensure its quality and suitability for analysis.
    • Exploratory Data Analysis (EDA):
      • Develop skills in exploring and visualizing data to uncover patterns, trends, and outliers, facilitating a deeper understanding of the dataset.
    • Statistical Analysis:
      • Introduce statistical methods and concepts to enable participants to draw meaningful insights and conclusions from data.
    • Machine Learning:
      • Provide an overview of machine learning algorithms and techniques, covering topics such as supervised and unsupervised learning, regression, classification, and clustering.
    • Programming Proficiency:
      • Foster proficiency in programming languages commonly used in data science, such as Python or R, and teach participants to implement algorithms and manipulate data.
    • Tools and Technologies:
      • Familiarize participants with popular data science tools and technologies, including Jupyter notebooks, pandas, NumPy, scikit-learn, and others.
    • Real-World Applications:
      • Offer hands-on projects and case studies that allow participants to apply their knowledge to practical, real-world scenarios, enhancing their problem-solving skills.
    • Ethical Considerations:
      • Discuss the ethical implications of working with data, emphasizing privacy, responsible data handling, and ethical decision-making in the context of data science.
    • Communication Skills:
      • Develop effective communication skills to convey complex findings and insights to both technical and non-technical stakeholders.
    • Project Management:
      • Introduce project management principles relevant to data science projects, emphasizing organization, planning, and execution.
    • Continuous Learning:
      • Encourage a mindset of continuous learning by fostering an awareness of current trends, emerging technologies, and industry best practices in data science.
    • Certification:
      • Provide participants with a recognized certification upon successful completion of the course, validating their proficiency in data science and enhancing their credibility in the job market.
  • By addressing these objectives, a data science course aims to equip participants with the skills and knowledge necessary to excel in the rapidly evolving and multidisciplinary field of data science.

  • Completing a data science certification course can significantly boost your career growth by opening up various opportunities in a rapidly evolving job market. Here are some potential avenues for career advancement after completing a data science course:
  • Data Scientist:
    • Acquire roles as a data scientist, where you can apply your skills in data analysis, machine learning, and statistical modeling to extract valuable insights and inform decision-making processes.
  • Machine Learning Engineer:
    • Specialize in machine learning and work on developing and implementing advanced algorithms for predictive modeling, recommendation systems, and other machine learning applications.
  • Business Intelligence Analyst:
    • Leverage your data analysis skills to help organizations make informed business decisions by interpreting and presenting data trends and insights.
  • Data Engineer:
    • Focus on designing, constructing, and maintaining the architecture that allows for the effective collection, storage, and retrieval of large volumes of data.
  • Big Data Engineer:
    • Specialize in handling and analyzing large-scale datasets using technologies like Hadoop and Spark, contributing to the management of big data infrastructure.
  • AI Researcher:
    • Engage in research and development activities in artificial intelligence, exploring innovative solutions to complex problems and contributing to advancements in the field.
  • Data Analyst:
    • Work as a data analyst, responsible for collecting, processing, and analyzing data to support business decisions and strategies.
  • Business Analyst:
    • Apply your data-driven insights to provide valuable information and recommendations that contribute to organizational strategies and goals.
  • Data Science Consultant:
    • Offer your expertise as a consultant, helping businesses across various industries implement effective data science solutions and strategies.
  • Entrepreneurship:
    • Start your own data science consultancy or venture, providing specialized services and solutions to businesses seeking to harness the power of data.
  • Specialized Roles:
    • Explore niche roles such as healthcare data analyst, financial data scientist, or marketing analytics specialist, depending on your interests and the industry you want to focus on.
  • Academic and Research Positions:
    • Pursue roles in academia or research institutions, contributing to the development of new methodologies, algorithms, and theories in the field of data science.
  • Career Progression:
    • With experience, you can progress to leadership positions such as Data Science Manager, Director of Analytics, or Chief Data Officer, where you oversee and guide data science initiatives within an organization.
  • Continuous learning and staying updated on emerging technologies will further enhance your career prospects. Networking within the data science community, attending conferences, and participating in ongoing professional development will contribute to sustained career growth in this dynamic field.

  • As per my Industry knowledge, I understand that fresh graduates entering the workforce have varying expectations when it comes to salary. It's crucial to consider factors such as the individual's education, relevant skills, the market demand for their expertise, and the industry standards. Typically, entry-level positions may have a standard salary range based on the industry and location.
  • The salary expectations for freshers entering the field of data science can vary based on factors such as educational qualifications, the specific role, location, and the company's size and industry.
  • When determining a fair compensation package for freshers, we take into account the following:
    • Data Analyst:
      • In countries like the United States, the starting salary for entry-level data analysts may range from $50,000 to $70,000 per year.
      • In India, the entry-level salaries for data analysts could range from ₹4,00,000 to ₹6,00,000 per annum.
    • Data Scientist:
      • In the United States, entry-level data scientists might expect a salary between $60,000 and $90,000 per year.
      • In India, entry-level data scientists may have starting salaries ranging from ₹6,00,000 to ₹8,00,000 per annum.
    • Machine Learning Engineer:
      • In the United States, entry-level machine learning engineers could have salaries ranging from $70,000 to $100,000 per year.
      • In India, starting salaries for machine learning engineers might range from ₹8,00,000 to ₹10,00,000 per annum.
    • Junior Data Engineer:
      • In the United States, junior data engineers might expect salaries between $60,000 and $80,000 per year.
      • In India, starting salaries for junior data engineers could range from ₹5,00,000 to ₹7,00,000 per annum.
  • It's important to note that these figures are approximate and can vary based on the factors mentioned earlier. Additionally, salary structures may have changed since my last update, and it's recommended to check the most recent industry reports or consult with professionals in your specific location and industry for the latest information. Additionally, gaining relevant certifications, advanced degrees, or demonstrating proficiency in in-demand skills can positively impact salary negotiations for entry-level data science positions.

  • Learning a data science certification course can offer a multitude of benefits, making it a valuable investment for individuals in various fields. Here are compelling reasons why you should consider pursuing a data science certification:
  • High Demand for Data Science Skills:
    • The demand for data scientists and professionals with data science skills is consistently high across industries. Organizations are increasingly relying on data-driven insights for decision-making, creating a robust job market for individuals with data science expertise.
  • Career Opportunities and Advancement:
    • Completing a data science certification enhances your employability and opens up diverse career opportunities. It can lead to roles such as data analyst, data scientist, machine learning engineer, and more, providing a pathway for career growth and advancement.
  • Competitive Advantage:
    • In today's competitive job market, having a certification in data science sets you apart from other candidates. It demonstrates your commitment to acquiring specialized skills and staying relevant in a rapidly evolving field.
  • Versatility Across Industries:
    • Data science skills are applicable across a wide range of industries, including finance, healthcare, marketing, technology, and more. This versatility allows you to explore diverse career paths and industries based on your interests.
  • Problem-Solving Skills:
    • Data science involves solving complex problems using analytical and statistical approaches. The skills learned in a data science course enhance your ability to approach challenges methodically, fostering critical thinking and problem-solving skills.
  • Innovation and Decision-Making:
    • Data science enables organizations to innovate by leveraging data to derive insights and make informed decisions. By learning data science, you contribute to your organization's ability to innovate and stay competitive in the marketplace.
  • Higher Earning Potential:
    • Professionals with data science skills often command competitive salaries due to the specialized nature of their expertise. Completing a certification can contribute to negotiating higher compensation packages.
  • Continuous Learning and Adaptability:
    • The field of data science is dynamic, with ongoing advancements and new technologies. Learning data science equips you with a mindset of continuous learning, allowing you to adapt to evolving industry trends and stay at the forefront of your field.
  • Global Relevance:
    • Data science is a globally recognized and sought-after skill. A certification in data science provides you with credentials that are recognized internationally, expanding your career opportunities beyond regional boundaries.
  • Contribution to Business Success:
    • Data science skills enable you to contribute directly to business success by providing actionable insights, improving decision-making processes, and optimizing operations.
  • Whether you are entering the job market, seeking career advancement, or looking to pivot into a new field, learning data science through a certification course can be a strategic and beneficial investment in your professional development.

  • Role of a Data Science Professional:
  • The role of a Data Science certified professional is diverse and multifaceted, encompassing a range of responsibilities aimed at extracting meaningful insights from data to inform decision-making processes. Here are key aspects of the role of a Data Science certified individual:
    • Data Analysis:
      • Analyze large and complex datasets to identify patterns, trends, and correlations that provide valuable insights into business processes, customer behavior, and other relevant domains.
    • Machine Learning:
      • Develop and implement machine learning models to predict outcomes, classify data, and automate decision-making processes. This involves selecting appropriate algorithms, training models, and evaluating their performance.
    • Statistical Analysis:
      • Apply statistical methods to interpret data and draw meaningful conclusions. This includes hypothesis testing, regression analysis, and other statistical techniques to validate findings and ensure data accuracy.
    • Data Cleaning and Preprocessing:
      • Prepare data for analysis by cleaning and preprocessing it. This involves handling missing values, outliers, and ensuring data quality to enhance the reliability of analytical results.
    • Data Visualization:
      • Communicate findings effectively through data visualization techniques. Use tools like charts, graphs, and dashboards to present complex information in a clear and understandable manner to both technical and non-technical stakeholders.
    • Feature Engineering:
      • Engineer features or variables that contribute to the performance of machine learning models. This involves selecting, transforming, and creating relevant features to improve the accuracy and efficiency of predictive models.
    • Collaboration with Stakeholders:
      • Collaborate with various teams, including business analysts, domain experts, and decision-makers, to understand business requirements and align data science solutions with organizational goals.
    • Ethical Considerations:
      • Uphold ethical standards in handling data, ensuring privacy, and addressing potential biases in models. Consider the ethical implications of data science solutions and make recommendations for responsible data use.
    • Continuous Learning:
      • Stay abreast of the latest advancements in data science, machine learning, and related technologies. Maintain a commitment to continuous learning to adapt to evolving industry trends.
    • Project Management:
      • Manage data science projects, including defining objectives, timelines, and deliverables. Coordinate with cross-functional teams to ensure successful implementation of data science solutions.
    • Optimization and Automation:
      • Optimize existing processes and automate repetitive tasks using data science techniques. This can lead to increased efficiency and productivity within an organization.
    • Documentation:
      • Document methodologies, processes, and findings to create a transparent and reproducible workflow. This is crucial for knowledge sharing and future reference.
    • Contribution to Innovation:
      • Contribute to the innovation within an organization by proposing and implementing data-driven solutions that address business challenges and uncover new opportunities.
  • The role of a Data Science certified professional is dynamic, and individuals in this field play a crucial role in leveraging data for strategic decision-making and organizational success. The specific responsibilities can vary based on the industry, organization size, and the focus of the data science projects.

  • I can provide insights into the top Hiring and Hiring trends and qualifications sought after for Data Science Professional positions in today's competitive job market.
  • Top Hiring for Data Science Professional
    • Technology and IT:
      • Technology companies, including major tech giants and startups, hire data scientists for roles related to product development, optimization, and data-driven innovation.
    • Finance and Banking:
      • Financial institutions leverage data science to enhance risk management, fraud detection, customer analytics, and algorithmic trading.
    • Healthcare and Life Sciences:
      • Healthcare organizations utilize data science for predictive modeling, patient outcomes analysis, drug discovery, and personalized medicine.
    • E-commerce and Retail:
      • Retailers and e-commerce platforms hire data scientists to optimize pricing strategies, personalize customer experiences, and improve supply chain efficiency.
    • Telecommunications:
      • Telecom companies use data science for network optimization, customer churn prediction, and the development of new services based on consumer behavior analysis.
    • Manufacturing and Supply Chain:
      • Data science is applied in manufacturing for process optimization, quality control, predictive maintenance, and supply chain management.
    • Energy and Utilities:
      • Energy companies leverage data science for predictive maintenance of equipment, energy consumption optimization, and smart grid analytics.
    • Consulting and Professional Services:
      • Consulting firms and professional services organizations hire data scientists to provide expertise to clients across various industries, helping them make data-driven decisions.
    • Government and Public Sector:
      • Government agencies use data science for policy analysis, fraud detection, cybersecurity, and improving public services.
    • Education and Research:
      • Academic institutions and research organizations hire data scientists for educational programs, research projects, and the development of data-driven solutions in diverse fields.
    • Media and Entertainment:
      • Media companies utilize data science for content recommendation, audience analytics, and advertising optimization.
    • Insurance:
      • Insurance companies employ data scientists for risk assessment, claims processing optimization, and customer segmentation.
    • Automotive:
      • Automotive companies apply data science in areas such as predictive maintenance, supply chain optimization, and the development of autonomous vehicles.
    • Agriculture:
      • Agricultural organizations use data science for precision farming, crop monitoring, and yield prediction.
    • Pharmaceuticals:
      • Pharmaceutical companies employ data scientists for drug discovery, clinical trial optimization, and personalized medicine initiatives.
  • Top Hiring Trends for Data Science Professional:
    • Remote Work Opportunities:
      • The rise of remote work has expanded opportunities for data science professionals to work with organizations globally, providing greater flexibility in job locations.
    • Industry-Specific Expertise:
      • Companies increasingly seek data scientists with domain-specific expertise, such as healthcare, finance, or e-commerce, to better align data solutions with industry challenges and objectives.
    • Emphasis on Soft Skills:
      • In addition to technical skills, employers value soft skills such as communication, collaboration, and the ability to convey complex findings to non-technical stakeholders.
    • Focus on Data Ethics and Privacy:
      • With growing concerns about data ethics and privacy, organizations prioritize hiring data scientists who understand and prioritize ethical considerations in their work.
    • Machine Learning Operations (MLOps):
      • MLOps, which involves the integration of machine learning models into the software development and deployment lifecycle, has become a crucial trend. Employers seek professionals skilled in deploying and maintaining machine learning models in real-world applications.
    • Automated Machine Learning (AutoML):
      • Hybrid roles that combine data science with expertise in areas like data engineering, DevOps, or business analysis are becoming more common as organizations seek well-rounded professionals who can contribute to end-to-end data solutions.
    • Continued Demand for Cloud Skills:
      • Proficiency in cloud platforms, particularly AWS, Azure, and Google Cloud, remains a sought-after skill as organizations migrate towards cloud-based data storage, processing, and analytics solutions.
    • Explainable AI (XAI):
      • There is an increased focus on developing machine learning models that are transparent and explainable. Employers value data scientists who can create models that provide clear insights into decision-making processes.
    • Upskilling and Continuous Learning:
      • The fast-paced evolution of data science technologies requires professionals to engage in continuous upskilling. Employers value candidates who demonstrate a commitment to staying updated on the latest industry trends.
    • Diversity and Inclusion Initiatives:
      • Companies are placing greater emphasis on building diverse and inclusive teams. There is an increased effort to hire data science professionals from diverse backgrounds and perspectives.
    • Real-Time Analytics:
      • The demand for real-time analytics capabilities is growing, and employers seek professionals who can develop and implement solutions for analyzing data in real-time.
    • Quantum Computing Awareness:
      • While not yet mainstream, there is a growing interest in quantum computing. Organizations may value data scientists who are aware of the potential applications and implications of quantum computing in data science.
  • Keep in mind that the field of data science is dynamic, and hiring trends may evolve. It's advisable to stay informed about the latest industry developments, job market trends, and emerging technologies to enhance your competitiveness as a data science professional.

  • Embark on an enriching journey into the world of data science with IT Training Classes, your premier destination for a comprehensive Data Science Certification Course in Gurgaon. Our program is meticulously designed to equip you with the skills and knowledge essential for a thriving career in data science.
    • Holistic Curriculum:
      • Immerse yourself in a holistic curriculum covering the fundamentals and advanced concepts of data science. Explore topics such as statistical analysis, machine learning, data visualization, and more.
    • Expert Instructors:
      • Learn from seasoned instructors with extensive industry experience. Our instructors are passionate about data science and are dedicated to providing high-quality education to help you succeed.
    • Hands-On Learning:
      • Embrace a hands-on learning approach with practical projects and real-world applications. Gain invaluable experience applying data science techniques to solve complex problems.
    • Industry-Relevant Projects:
      • Engage in hands-on projects that mirror the challenges faced by data scientists in real-world scenarios. Build a robust portfolio showcasing your proficiency to potential employers.
    • Cutting-Edge Technologies:
      • Stay at the forefront of data science with a curriculum that incorporates the latest tools and technologies. Acquire skills in Python, R, TensorFlow, and other industry-standard tools.
    • Career Support:
      • Beyond the course, receive comprehensive career support services, including resume enhancement, interview preparation, and job placement assistance. Benefit from our connections with industry professionals
    • Community and Networking:
      • Join a dynamic community of learners and professionals in the field of data science. Collaborate, network, and share knowledge with peers, alumni, and industry experts.
    • Flexible Learning Options:
      • Tailor your learning experience with flexible options, including in-person classes, online learning, or a hybrid approach. Choose a schedule that aligns with your preferences and commitments.
    • Continuous Learning Opportunities:
      • Elevate your skills with continuous learning opportunities, including workshops, webinars, and resources. Stay updated with emerging trends and advancements in data science.
    • Success Stories:
      • Celebrate the success of our graduates who have achieved remarkable milestones in the field of data science. Their accomplishments are a testament to the effectiveness of our programs and their professional growth.
    • Certification:
      • Upon successful completion of our Data Science Certification Course, receive an official certificate recognizing your dedication and proficiency. Showcase this credential in your portfolio or resume, demonstrating your commitment to excellence in data science.
  • Embark on your data science journey with IT Training Classes, where we are dedicated to providing a comprehensive education in data science, empowering you for a successful and rewarding career in this dynamic field.

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Course Duration 40-45 Days 7 Weekends 8 Days
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Data Science Certification Training

  • Data Science stands out as a potent analytical platform designed to unearth valuable discoveries within vast datasets. This Master's Program in Data Science is a holistic journey encompassing various dimensions of computer science. It delves into the realms of data visualization, analytics, statistical methodologies, and proficiency in both R and Python programming languages. The program's focal point is the transformation of extensive datasets into meaningful and actionable content.
  • A distinctive feature of this program is its inclusive coverage of Machine Learning and Deep Learning, along with the integration of Data Analytics and Cloud Implementation. This reflects a forward-thinking approach that aligns with the dynamic landscape of data science. This extended practical engagement allows students to apply their acquired skills in real-world projects, ensuring a seamless transition from theoretical understanding to hands-on, industry-relevant experience.
    • SQL

    • Introduction to Data Science
    • Life cycle of Data Science
    • Skills required for Data Science
    • Applications of Data Science in different industries

  • Introduction
    • What is Python
    • Why does Data Science require Python
    • Installation of Anaconda
    • Understanding Jupyter Notebook
    • Basic commands for Jupyter Notebook
    • Understanding Python Syntax
  • Data types and Data Structures
    • Variables and Strings
    • List, Sets, Tuples and Dictionaries
  • Control flow and Conditional Statements
    • Conditional Oper ators, Arithmetic Oper ators and Logical Operators
    • If, Elif and Else Statements
    • While Loops
    • For Loops
    • Nested Loops and List and Dictionary Compr ehensions
  • Functions
    • What is function and types of functions
    • code optimization and argument functions
    • Scope
    • Lambda Functions
    • Map, Filter and Reduce
  • File Handling
    • Create, Read, Write files and Oper ations in File Handling
    • E rrors and Exception Handling
  • Class and Objects
    • Create a class
    • Create a object
    • The_init_()
    • Modifying Objects
    • Object Methods
    • Self
    • Modify the object Properties
    • Delete Object
    • Pass Statements

    • Introduction to Array
    • Creation and Printing of and array
    • Basic Operations in Numpy
    • Indexing
    • Mathematical Functions of Numpy
  • Data Manipulation with Pandas
    • Series and Data Frames
    • Data Importing and Exporting through Excel, CSV Files
    • Data Understanding Operations
    • Indexing and Slicing and More Filtering with Conditional Slicing
    • Groupby, Pivot table and Cross Tab
    • Concatenating and Merging Joining
    • Descriptive Statistics
    • Removing Duplicates
    • String Manipulation
    • Missing Data Handling
  • Data Visualization using Matplotlib and Pandas
    • Introduction to Matplotlib
    • Basic Ploting
    • Properties of Ploting
    • About SubPlots
    • Line Plots
    • Pie chart and Bar Graph
    • Histograms
    • Box and Violin plots
    • Scatter Plots
  • Case Study on Exploratory Data Analysis (EDA) and Visualizations
    • What is EDA
    • Uni - Variate Analysis
    • Bi - Variate Analysis
    • More on Seaborn based Plotting Including paris plot, catplot, Heat Maps Count plot along with matplotlib plots
    • Regular Expressions
      • Structured Data and Unstructured Data
      • Literals and Meta Characters
      • How to Regular Expressions using Pandas?
      • Inbuilt Methods
      • Pattern Matching
      • Data Mining (WEB - SCRAPING)
        This project starts completely from scratch which involves collection of Raw Data from different sources and converting the unstructured data to a structured format to apply Machine Learning and NLP models.
        This project covers the main four steps of Data Science Life Cycle which involves
      • Data Collection
      • Data Mining
      • Data Preprocessing
      • Data visualization
        Ex:Text, CSV, TSV, Excel Files, Matrices, Images
  • Advanced Statistics
    • Data Types and Data Structures
      • Statistics in Data SCience
      • What is Statistics ?
      • How is Statistics used in Data Science ?
      • Population and Sample
      • Parameter and Statistic
      • variable and its types
    • Data Gathering Techniques
      • Data types
      • Data Collection Techniques
      • Sampling Techniques:
      • Convenience Sampling, Simple Random Sampling
      • Stratified Sampling, Systematic Sampling and Cluster Sampling
    • Descriptive Statistics
      • What is Univariate and Bi Variate Analysis ?
      • Measures of Central Tendencies
      • Measures of Dispersion
      • Skewness and Kurtosis
      • Box Plots and Outliers detection
      • Covariance and Correlation
    • Probability Distribution
      • Probability and Limitations
      • Discrete Probability Distributions
      • Bernoulli, Binomial Distribution, Poisson Distribution
      • Continuous Probability Distributions
      • Normal Distribution, Standard Normal Distribution
    • Inferential Statistics
      • Sampling Variability and Central Limit theorem
      • Confidence Intervals
      • Hypothesis Testing
      • Z-test, t-test
      • Chi-Square Test
      • F-Test and ANOVA
    • SQL for Data Science
      • Introduction to Databases
        • Basic of SQL
          • DML, DDL, DCL and Data Types
          • Common SQL commands using SELECT, FROM, and WHERE
          • Logical Operator in SQL
        • SQL Joins
          • INNER and OUTER joins to combine data from multiple tables
          • RIGHT, LEFT joins to combine data from multiple tables
        • Filtering and Sorting
          • Advanced filtering using IN, OR and NOT
          • Sorting with GROUPBY and ORDERBY
        • SQL Aggregations
          • Common Aggregations including COUNT, SUM, MIN
          • CASE and DATE functions as well as work with NULL values
        • Subqueries and Temp Tables
          • Subqueries to run multiple queries together
          • Temp tables to access a table with more than one query
        • SQL Data Cleaning
          • Perform Data Cleaning using SQL

      • What is Machine Learning
      • Supervised Versus Unsupervised Learning
      • Regression Versus Classification Problems Assessing Model Accuracy
    • Regression Techniques
      • Linear Regression
        • Sample Linear Regression
          • Estimating the Coefficients
          • Assessing the Coefficients Estimates
          • R Squared and Adjusted R Squared
          • MSE and RMSE
      • Multiple Linear Regression
        • Estimating the Regression Coefficients
          • OLS Assumptions
          • Multicollinearity
          • Feature Selection
          • Gradient Discent
      • Evaluating the Metrics of Regression Techniques
        • Homoscedasticity and Heter oscedasticity of error terms
          • Residual Analysis
          • Q-Q Plot
          • Cook's distance and Shapiro-Wilk Test
          • Identifying the line of best fit
          • Other Considerations in the Regression Model
          • Qualitative Predictors
          • Interaction Terms
          • Non-linear Transformations of the Predictors
      • Polynomial Regression
        • Why Polynomial Regression
        • Creating polynomial linear Regression
        • evaluating the metrics
      • Regularization Techniques
        • Lasso Regularization
        • Ridge Regularization
        • ElasticNet Regularization
      • Logistic regression
        • An Overview of Classification
        • Difference Between Regression and classification Models
        • Why Not linear Regression?
        • Logistics Regression
        • The Logistic Model
        • Estimation the Regression Coefficients and Making Predictions
        • Logit and Sigmoid functions
        • Setting the threshold and understanding decision boundary
        • Logistic Regression for >2 Response Classes
        • Evaluation Metrics for Classification Models:
        • Confusion Matrix
        • Accuracy and Error rate
        • TPR and FPR
        • Precision and recall, F1 Score
        • AUC - ROC
        • Kappa Score
      • Naive Bayes
        • Principle of Naive Bayes Classifier
        • Bayes Theorem
        • Terminology in Naive Bayes
        • Posterior probability
        • Prior probability of class
        • Likelihood
        • Types of Naive Bayes Classifier
        • Multinomial Naive Bayes
        • Bernoulli Naive Bayes and Gaussian Naive Bayes
      • Decision Trees
        • Decision Trees (Rule Based Learning):
        • Basic Terminology in Decision Tree
        • Root Node and Terminal Node
        • Regression Trees and Classification Trees
        • Trees Versus Linear Models
        • Advantages and Disadvantages of Trees
        • Gini Index
        • Overfitting and Pruning
        • Stopping Criteria
        • Accuracy Estimation using Decision Trees
      • Case Study: A Case Study on Decision Tree using Python
        • Resampling Methods:
        • Cross-Validation
        • The Validation Set Approach Leave-One-Out Cross-Validation
        • k-Fold Cross-Validation
        • Bias-Variance Trade-O fffor k-Fold Cross-Validation
      • Ensemble Methods in Tree Based Models
        • What is Ensemble Learning?
        • What is Bootstrap Aggregation Classifiers and how does it work?
      • Random Forest
        • What is it and how does it work?
        • Variable selection using Random Forest
      • Boosting: AdaBoost, Gradient Boosting
        • What is it and how does it work?
        • H yper parameter and Pro's and Con's
      • K Nearest Neighbors
        • K-Nearest Neighbor Algorithm
        • Eager Vs Lazy learners
        • How does the KNN algorithm work?
        • How do you decide the number of neighbors in KNN?
        • Curse of Dimensionality
        • Pros and Cons of KNN
        • How to improve KNN performance
    • Case Study: A Case Study on k-NN using Python
      • Support Vector Machines
        • The Maximal Margin Classifier
        • HyperPlane
        • Support Vector Classifiers and Support Vector Machines
        • Hard and Soft Margin Classification
        • Classification with Non-linear Decision Boundaries
        • Kernel Trick
        • Polynomial and Radial
        • Tuning Hyper parameters for SVM
        • Gamma, Cost and Epsilon
        • SVMs with More than Two Classes
  • Case Study: A Case Study on SVM using Python
    • Why Unsupervised Learning ?
    • How it different form Supervised learning
    • The challenges of Unsupervised learning
    • Principle Components Analysis
      • Introduction to Dimensionality Reduction and it's necessity
      • What are principle Components ?
      • Demonstration of 2D PCA and 3D PCA
      • EigenValues , EigenVectors and Orthogonality
      • Transforming EigenValues into a new data set
      • Proportion of variance explained in PCA
      • Case Study: A Case Study on PCA usong Python

    • K - Means Clustering
      • Centroids and Medoids
      • Deciding Optimal values of 'k' using Elbow Method
      • Linkage Method
    • Hierarchical Clustering
      • Divisive and Agglomerative Clustering
      • Dendrograms and their interpretation
      • Applications of Clustering
      • Practical Issues in Clustering
      • Case Study : A case study on clustering using python

    • Associate Rules
      • Market Basket Analysis
    • Apriori
      • Metric Support/Confidence/Lift
      • Improving Supervised Learning algorithms with clustering
      • case study : A case study on association rules using pyhton

    • Recomendation Systems
      • What are recommendation engines?
      • How does a recommendation engine work?
      • Data collection
      • Data storage
      • Filtering the data
      • Content based filtering
      • Collaborative filtering
      • Cold start problem
      • Matrix factorization
      • Building a recommendation engine using matrix factorization
      • Case Study

    • Introduction to Neural Networks
      • Introduction to Perceptron & History of Neural networks
      • Activation functions (a)Sigmoid (b) Relu (c)Softmax (d)Leaky Relu (e)Tanh
      • Gradient Descent
      • Learning Rate and tuning
      • Optimization functions
      • Introduction to Tensorflow
      • Introduction to keras
      • Back propagation and chain rule
      • Fully connected layer
      • Cross entropy
      • Weight Initialization
      • Regularization
    • TensorFlow 2.0
      • Introducing Google Colab
      • Tensorflow basic syntax
      • Tensorflow Graphs
      • Tensorboard
    • Artificial Neural Network with Tensorflow
      • Neural Network for Regression
      • Neural Network for Classification
      • Evaluating the ANN
      • Improving and tuning the ANN
      • Saving and Restoring Graphs

    • UNIT 1: Working with images & CNN Building Blocks
      • Working with Images_Introduction
      • Working with Images - Reshaping understanding, size of image understanding pixels Digitization, Sampling, and Quantization
      • Working with images - Filtering
      • Hands-on Python Demo: Working with images
      • Introduction to Convolutions
      • 2D convolutions for Images
      • Convolution - Backward
      • Transposed Convolution and Fully Connected Layer as a Convolution
      • Pooling: Max Pooling and Other pooling options
    • UNIT 2: CNN Architectures and Transfer Learning
      • CNN Architectures and LeNet Case Study
      • Case Study: AlexNet
      • Case Study: ZFNet and VGGNet
      • Case Study: GoogleNet
      • Case Study: ResNet
      • GPU vs CPU
      • Transfer Learning Principles and Practice
      • Hands-on Keras Demo: SVHN Transfer learning from MNIST dataset
      • Transfer learning Visualization (run package, occlusion experiment)
      • Hands-on demo -T-SNE
    • UNIT 3: Object Detection
      • CNN's at Work - Object Detection with region proposals
      • CNN's at Work - Object Detection with Yolo and SSD
      • Hands-on demo- Bounding box regressor
        #Need to do a semantic segmentation project
    • UNIT 4: CNN's at Work - Semantic Segmentation
      • CNNs at Work - Semantic Segmentation
      • Semantic Segmentation process
      • U-Net Architecture for Semantic Segmentation
      • Hands-on demo - Semantic Segmentation using U-Net
      • Other variants of Convolutions
      • Inception and MobileNet models
    • UNIT 5: CNN's at work- Siamese Network for Metric Learning
      • Metric Learning
      • Siamese Network as metric learning
      • How to train a Neural Network in Siamese way
      • Hands-on demo - Siamese Network

    • Unit 1: Introduction to Statistical NLP Techniques
      • Introduction to NLP
      • Preprocessing , NLP Tokenization, stop words, normalization, Stemming and lemmatization
      • Preprocessing in NLP Bag of words,TF-IDF as features
      • Language model probabilistic models, n-gram model and channel model
      • Hands on NLTK
    • Unit 2 : Word embedding
      • Word2vec
      • Golve
      • POS Tagger
      • Named Entity Recognition(NER)
      • POS with NLTK
      • TF-IDF with NLTK
    • Unit 3: Sequential Models
      • Introdcution to sequential models
      • Introduction to RNN
      • Intro to LSTM
      • LSTM forward pass
      • LSTM backprop through time
      • Hands on keras LSTM
    • Unit 4 : Applications
      • Sentiment Analysis
      • Sentence generation
      • Machine translation
      • Advanced LSTM structures
      • Keras- machine translation
      • ChatBot

      • Install Tableau for Desktop 10
      • Tableau to Analyze Data
      • Connect Tableau to a variety of dataset
      • Analyze, Blend, Join and Calculate Data
      • Tableau to Visualize Data
      • Visualize Data In the form of Various Charts, Plots and Maps
      • Data Hierarchies
      • Work with Data Blending in Tableau
      • Work with Parameters
      • Create Calculated Fields
      • Adding Filters and Quick Filters
      • Create Interactive Dashboards
      • Adding Actions to Dashboards


    • Choosing IT Training Classes for your Data Science certification course in Gurgaon offers several compelling reasons:
    • Expert Faculty:
      • Our institute boasts a team of experienced and knowledgeable instructors in the field of Data Science. They bring real-world expertise to the classroom.
    • Comprehensive Curriculum:
      • Our Data Science certification course covers a wide array of topics, ensuring you receive a well-rounded education, including the latest tools, techniques, and industry practices.
    • Hands-on Learning:
      • We prioritize practical, hands-on learning experiences. Our courses include projects and real-world applications, allowing you to apply theoretical knowledge in a practical setting.
    • Industry-Relevant Skills:
      • The curriculum is designed to equip you with skills that are directly applicable to the current industry demands, making you job-ready upon completion.
    • State-of-the-Art Facilities:
      • Our institute in Gurgaon provides a conducive learning environment with modern facilities and infrastructure, enhancing your overall learning experience.
    • Placement Assistance:
      • We offer placement assistance to help you kickstart your career in Data Science. Our connections with industry partners and job placement support contribute to your success.
    • Flexible Learning Options:
      • Recognizing the diverse needs of our students, we provide flexible learning options, including weekend classes, online modules, and personalized learning plans.
    • Networking Opportunities:
      • Engage with a community of like-minded individuals, fostering networking opportunities with fellow students, alumni, and industry professionals.
    • Continuous Learning Support:
      • Our commitment to your success extends beyond the classroom. We provide ongoing support, resources, and updates to keep you informed about advancements in Data Science.
    • Proximity to Industry Hub:
      • Situated in Gurgaon, our institute benefits from its proximity to the thriving IT and corporate sector, offering exposure to industry events, seminars, and potential job opportunities.
    • At IT Training Classes, we prioritize your success in your Data Science journey, offering a holistic and enriching learning experience in the vibrant city of Gurgaon.

    • Enrolling in Data Science Certification Training can significantly enhance your job prospects in Gurgaon for several reasons:
      • Skill Development:
        • Certification training equips you with the essential skills required in the field of Data Science, including programming languages (Python, R), statistical analysis, machine learning, and data visualization.te with skills that are in high demand, increasing your chances of landing a good job.
      • Industry Recognition:
        • Having a recognized Data Science certification adds credibility to your resume. Employers often prefer candidates with validated skills through reputable certification programs.
      • Job Readiness:
        • A comprehensive certification program prepares you for the practical challenges of the job. You'll gain hands-on experience through projects, case studies, and real-world applications, making you job-ready.
      • Current Industry Relevance:
        • Data Science certification courses are designed to cover the latest tools, techniques, and trends in the industry. This ensures that you are well-versed in the most current practices.
      • Networking Opportunities:
        • Many certification programs provide opportunities to connect with industry professionals, attend networking events, and access career resources. Building a professional network is crucial for job opportunities in Gurgaon.
      • Placement Assistance:
        • Reputable training institutes often offer placement assistance. This includes resume building, interview preparation, and connecting you with potential employers in Gurgaon's thriving job market.
      • Demonstrated Expertise:
        • Certification serves as tangible proof of your expertise in Data Science. Employers can quickly identify your proficiency, making you a desirable candidate for Data Science roles.
      • Access to Industry Insights:
        • During the certification program, you may gain insights into industry best practices, emerging technologies, and real-world applications. This knowledge can set you apart during job interviews.
      • Career Transition Support:
        • If you are transitioning to a career in Data Science, certification training provides a structured pathway and support to navigate the challenges of switching careers successfully.
      • Competitive Edge:
        • In the competitive job market of Gurgaon, a Data Science certification gives you a competitive edge. It distinguishes you from other candidates and demonstrates your commitment to continuous learning.
    • By investing in Data Science Certification Training, you not only acquire the skills needed for a Data Science job but also position yourself as a strong candidate in the job market in Gurgaon.

    Within the bustling domain of data science education, IT Training Classes stands as a beacon, fostering a thriving community of over 4000 enthusiasts in Gurgaon through our Data Science Certification Course. The distinctiveness of our program lies in its adaptability, meticulously tailoring its curriculum to meet the distinctive needs of individual learners and corporate entities navigating the dynamic landscape of data science. As you embark on this certification journey with IT Training Classes, our comprehensive package unveils a trove of resources—engaging online videos, immersive hands-on projects, and project source materials all aimed at elevating the learning experience and ensuring practical application in the field. The affordability of this investment extends a welcoming hand to anyone with the ambition to excel in this rapidly evolving and impactful field. Join the vibrant data science community at IT Training Classes in Gurgaon, where knowledge converges with innovation, and aspirations find a path to success.

    • As a general guideline:
      • Entry-Level (0-2 years of experience):
        • Data Analysts or Junior Data Scientists might earn between ₹4 to ₹8 lakhs per annum.
      • Mid-Level (2-5 years of experience):
        • Data Scientists with a few years of experience could earn between ₹8 to ₹15 lakhs per annum.
      • Senior-Level (5+ years of experience):
        • Senior Data Scientists or Data Science Managers may earn upwards of ₹15 lakhs and beyond, depending on experience and expertise.
      • Specialized Roles (e.g., Machine Learning Engineer, AI Specialist):
        • Professionals with expertise in specialized areas of Data Science might command higher salaries, and it could range from ₹15 lakhs to ₹25 lakhs or more.
      • Location Impact:
        • Salaries can vary significantly based on the city or region. For example, salaries in cities like Bengaluru, Hyderabad, and Pune are often higher compared to other regions.
      • Industry and Company Size:
        • Data Science professionals working in industries like IT, e-commerce, finance, and healthcare may have variations in salary. Larger companies might offer higher salaries compared to smaller ones.

    • Becoming a successful Data Scientist requires a diverse skill set that encompasses technical, analytical, and domain-specific expertise. Here are some of the topmost skills required to transition into a Data Scientist role:
      • Programming Skills:
        • Languages:
          • Proficiency in languages such as Python or R is essential. These languages are widely used for data manipulation, analysis, and machine learning.
        • SQL:
          • Ability to work with relational databases and write SQL queries for data extraction and manipulation.
      • Statistical Knowledge:
        • Understanding of statistical concepts and methods is crucial for analyzing data and drawing meaningful insights.
      • Machine Learning and AI:
        • Familiarity with machine learning algorithms and frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch) is essential for building predictive models and solving complex problems.
      • Data Cleaning and Preprocessing:
        • Skills in cleaning and preprocessing data, handling missing values, and dealing with outliers to ensure data quality.
      • Data Visualization:
        • Proficiency in data visualization tools (e.g., Matplotlib, Seaborn, Tableau) to communicate findings effectively to both technical and non-technical stakeholders.
      • Big Data Technologies:
        • Knowledge of big data technologies like Hadoop, Spark, and distributed computing frameworks for handling large datasets.
      • Domain Knowledge:
        • Understanding of the specific industry or domain you are working in is crucial for contextualizing data and deriving meaningful insights.
      • Communication Skills:
        • Ability to communicate complex findings in a clear and understandable manner to both technical and non-technical audiences.
      • Problem-Solving Skills:
        • Strong analytical and problem-solving skills to approach complex business challenges and devise data-driven solutions.
      • Business Acumen:
        • Understanding of business processes and goals to align data science initiatives with organizational objectives.
      • Continuous Learning:
        • The field of data science is dynamic, and staying updated with the latest tools, techniques, and industry trends is crucial.
      • Collaboration and Teamwork:
        • Working collaboratively with cross-functional teams, including engineers, business analysts, and domain experts, to achieve common goals.
      • Ethical Considerations:
        • Awareness of ethical considerations and privacy concerns related to handling and analyzing sensitive data.

    Here, you will receive training from highly qualified faculty members working in the industry for numerous years.

    For details information & FREE demo class, call us at +91-9717614047 or Email ua at: [email protected]
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