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Data Science

Lessons In This Class

  • 1. Understanding Data Science
  • 2. The Data Science Life Cycle
  • 3. Understanding Artificial Intelligence (AI)
  • 4. Overview of Implementation of Artificial Intelligence
  • 5. Machine Learning
  • 6. Deep Learning
  • 7. Artificial Neural Networks (ANN)
  • 8. Natural Language Processing (NLP)
  • 9. How R connected to Machine Learning
  • 10. R - as a tool for Machine Learning Implementation
  • 1. What is Python and history of Python
  • 2. Python-2 and Python-3 differences
  • 3. Install Python and Environment Setup
  • 4. Python Identifiers, Keywords and Indentation
  • 5. Comments and document interlude in Python
  • 6. Command line arguments and Getting User Input
  • 7. Python Basic Data Types and Variables
  • 1. Understanding Lists in Python
  • 2. Understanding Iterators
  • 3. Generators, Comprehensions and Lambda Expressions
  • 4. Understanding and using Ranges.
  • 1. Introduction to the section
  • 2. Python Dictionaries and More on Dictionaries
  • 3. Sets and Python Sets Examples
  • 1. Reading and writing text files
  • 2. Appending to Files
  • 3. Writing Binary Files Manually and using Pickle Module
  • 1. Python user defined functions
  • 2. Python packages functions
  • 3. The anonymous Functions
  • 4. Loops and statement in Python
  • 5. Python Modules & Packages
  • 1. What is Exception?
  • 2. Handling an exception
  • 3. try….except…else
  • 4. try-finally clause
  • 5. Argument of an Exception
  • 6. Python Standard Exceptions
  • 7. Raising an exceptions
  • 8. User-Defined Exceptions
  • 1. What are regular expressions?
  • 2. The match Function and the Search Function
  • 3. Matching vs Searching
  • 4. Search and Replace
  • 5. Extended Regular Expressions and Wildcard
  • 1. Collections – named tuples, default dicts
  • 2. Debugging and breakpoints, Using IDEs
  • 1. Understanding different types of Data
  • 2. Understanding Data Extraction
  • 3. Managing Raw and Processed Data
  • 4. Wrangling Data using Python
  • 5. Using Mean, Median and Mode
  • 6. Variation and Standard Deviation
  • 7. Probability Density and Mass Functions
  • 8. Understanding Conditional Probability
  • 9. Exploratory Data Analysis (EDA)
  • 10. Working with Numpy, Scipy and Pandas
  • 1. Understand what is a Machine Learning Model
  • 2. Various Machine Learning Models
  • 3. Choosing the Right Model
  • 4. Training and Evaluating the Model
  • 5. Improving the Performance of the Model
  • 1. Understanding Predictive Model
  • 2. Working with Linear Regression
  • 3. Working with Polynomial Regression
  • 4. Understanding Multi Level Models
  • 5. Selecting the Right Model or Model Selection
  • 6. Need for selecting the Right Model
  • 7. Understanding Algorithm Boosting
  • 8. Various Types of Algorithm Boosting
  • 9. Understanding Adaptive Boosting
  • 1. Understanding the Machine Learning Algorithms
  • 2. Importance of Algorithms in Machine Learning
  • 3. Exploring different types of Machine Learning Algorithms
  • 4. Supervised Learning
  • 5. Unsupervised Learning
  • 6. Reinforcement Learning
  • 1. Understanding the Supervised Learning Algorithm
  • 2. Understanding Classifications
  • 3. Working with different types of Classifications
  • 4. Learning and Implementing Classifications
  • 5. Logistic Regression
  • 6. Naïve Bayes Classifier
  • 7. Nearest Neighbour
  • 8. Support Vector Machines (SVM)
  • 9. Decision Trees
  • 10. Boosted Trees
  • 11. Random Forest
  • 12. Time Series Analysis (TSA)
  • 13. Understanding Time Series Analysis
  • 14. Advantages of using TSA
  • 15. Understanding various components of TSA
  • 16. AR and MA Models
  • 17. Understanding Stationarity
  • 18. Implementing Forecasting using TSA
  • 1. Understanding Unsupervised Learning
  • 2. Understanding Clustering and its uses
  • 3. Exploring K-means
  • 4. What is K-means Clustering
  • 5. How K-means Clustering Algorithm Works
  • 6.Implementing K-means Clustering
  • 7. Exploring Hierarchical Clustering
  • 8. Understanding Hierarchical Clustering
  • 9. Implementing Hierarchical Clustering
  • 10. Understanding Dimensionality Reduction
  • 11. Importance of Dimensions
  • 12. Purpose and advantages of Dimensionality Reduction
  • 13. Understanding Principal Component Analysis (PCA)
  • 14. Understanding Linear Discriminant Analysis (LDA)
  • 1. What is Hypothesis Testing in Machine Learning
  • 2. Advantages of using Hypothesis Testing
  • 3. Basics of Hypothesis
  • 4. Normalization
  • 5. Standard Normalization
  • 6.Parameters of Hypothesis Testing
  • 7. Null Hypothesis
  • 8. Alternative Hypothesis
  • 9. The P-Value
  • 10. Types of Tests
  • 11. T Test
  • 12. Z Test
  • 13. ANOVA Test
  • 14. Chi-Square Test
  • 1. Understanding Reinforcement Learning Algorithm
  • 2. Advantages of Reinforcement Learning Algorithm
  • 3. Components of Reinforcement Learning Algorithm
  • 4. Exploration Vs Exploitation tradeoff
  • 1. What is R?
  • 2. History and Features of R
  • 3. Introduction to R Studio
  • 4. Installing R and Environment Setup
  • 5. Command Prompt
  • 6. Understanding R programming Syntax
  • 7. Understanding R Script Files
  • 1. Data types in R
  • 2. Creating and Managing Variables
  • 3. Understanding Operators
  • 4. Assignment Operators
  • 5. Arithmetic Operators
  • 6. Relational and Logical Operators
  • 7. Other Operators
  • 8. Understanding and using Decision Making Statements
  • 9.The IF Statement
  • 10. The IF…ELSE statement
  • 11. Switch Statement
  • 12. Understanding Loops and Loop Control
  • 13. Repeat Loop
  • 14. While Loop
  • 15. For Loop
  • 16. Controlling Loops with Break and Next Statements
  • 1. Understanding the Vector Data type
  • 2. Introduction to Vector Data type
  • 3. Types of Vectors
  • 4. Creating Vectors and Vectors with Multiple Elements
  • 5. Accessing Vector Elements
  • 6. Understanding Arrays in R
  • 7. Introduction to Arrays in R
  • 8. Creating Arrays
  • 9.Naming the Array Rows and Columns
  • 10. Accessing and manipulating Array Elements
  • 11. Understanding the Matrices in R
  • 12. Introduction to Matrices in R
  • 13. Creating Matrices
  • 14. Accessing Elements of Matrices
  • 15. Performing various computations using Matrices
  • 16. Understanding the List in R
  • 17. Understanding and Creating List
  • 18. Naming the Elements of a List
  • 19. Accessing the List Elements
  • 20. Merging different Lists
  • 21. Manipulating the List Elements
  • 22. Converting Lists to Vectors
  • 23. Understanding and Working with Factors
  • 24. Creating Factors
  • 25.Data frame and Factors
  • 26. Generating Factor Levels
  • 27. Changing the Order of Levels
  • 28. Understanding Data Frames
  • 29. Creating Data Frames
  • 30. Matrix Vs Data Frames
  • 31. Sub setting data from a Data Frame
  • 32. Manipulating Data from a Data Frame
  • 33. Joining Columns and Rows in a Data Frame
  • 34. Merging Data Frames
  • 35. Converting Data Types using Various Functions
  • 36. Checking the Data Type using Various Functions
  • 1. Understanding Functions in R
  • 2. Definition of a Function and its Components
  • 3. Understanding Built in Functions
  • 4. Character/String Functions
  • 5. Numerical and Statistical Functions
  • 6. Date and Time Functions
  • 7. Understanding User Defined Functions (UDF)
  • 8. Creating a User Defined Function
  • 9.Calling a Function
  • 10. Understanding Lazy Evaluation of Functions
  • 1. Understanding External Data
  • 2. Understanding R Data Interfaces
  • 3.Working with Text Files
  • 4. Working with CSV Files
  • 5. Understanding Verify and Load for Excel Files
  • 6. Using WriteBin() and ReadBin() to manipulate Binary Files
  • 7. Understanding the RMySQL Package to Connect and Manage MySQL Databases
  • 1. What is Data Visualization
  • 2. Understanding R Libraries for Charts and Graphs
  • 3. Using Charts and Graphs for Data Visualizations
  • 4. Exploring Various Chart and Graph Types
  • 5. Pie Charts and Bar Charts
  • 6. Box Plots and Scatter Plots
  • 7. Histograms and Line Graphs
  • 1. Understanding the Basics of Statistical Analysis
  • 2. Uses and Advantages of Statistical Analysis
  • 3. Understanding and using Mean, Median and Mode
  • 4. Understanding and using Linear, Multiple and Logical Regressions
  • 5. Generating Normal and Binomial Distributions
  • 6. Understanding Inferential Statistics
  • 7. Understanding Descriptive Statistics and Measure of Central Tendency
  • 1. Understanding Packages
  • 2. Installing and Loading Packages
  • 3. Managing Packages
  • 1. Understand what is a Machine Learning Model
  • 2. Various Machine Learning Models
  • 3. Choosing the Right Model
  • 4. Training and Evaluating the Model
  • 5. Improving the Performance of the Model
  • 1. Understanding Predictive Model
  • 2. Working with Linear Regression
  • 3. Working with Polynomial Regression
  • 4. Understanding Multi Level Models
  • 5. Selecting the Right Model or Model Selection
  • 6. Need for selecting the Right Model
  • 7. Understanding Algorithm Boosting
  • 8. Various Types of Algorithm Boosting
  • 9. Understanding Adaptive Boosting
  • 1. Understanding the Machine Learning Algorithms
  • 2. Importance of Algorithms in Machine Learning
  • 3. Exploring different types of Machine Learning Algorithms
  • 4. Supervised Learning
  • 5. Unsupervised Learning
  • 6. Reinforcement Learning
  • 1. Understanding the Supervised Learning Algorithms
  • 2. Understanding Classifications
  • 3. Working with different types of Classifications
  • 4. Learning and Implementing Classifications
  • 5. Logistic Regression
  • 6. Naïve Bayes Classifier
  • 7. Nearest Neighbor
  • 8. Support Vector Machines (SVM)
  • 9. Decision Trees
  • 10. Boosted Trees
  • 11. Random Forest
  • 12. Time Series Analysis (TSA)
  • 13. Understanding Time Series Analysis
  • 14. Advantages of using TSA
  • 15. Understanding various components of TSA
  • 16. AR and MA Models
  • 17. Understanding Stationarity
  • 18. Implementing Forecasting using TSA
  • 1. Understanding Unsupervised Learning
  • 2. Understanding Clustering and its uses
  • 3. Exploring K-means
  • 4. What is K-means Clustering
  • 5. How K-means Clustering Algorithm Works
  • 6. Exploring Hierarchical Clustering
  • 7. Understanding Hierarchical Clustering
  • 8. Implementing Hierarchical Clustering
  • 9. Understanding Dimensionality Reduction
  • 10. Importance of Dimensions
  • 11. Purpose and advantages of Dimensionality Reduction
  • 12. Understanding Principal Component Analysis (PCA)
  • 13. Understanding Linear Discriminant Analysis (LDA)
  • 1. What is Hypothesis Testing in Machine Learning
  • 2. Advantages of using Hypothesis Testing
  • 3. Basics of Hypothesis
  • 4. Normalization
  • 5. Standard Normalization
  • 6. Parameters of Hypothesis Testing
  • 7. Null Hypothesis
  • 8. Alternative Hypothesis
  • 9. The P-Value
  • 10. Types of Tests
  • 11. T Test
  • 12. Z Test
  • 13. ANOVA Test
  • 14. Chi-Square Test
  • 1. Understanding Reinforcement Learning Algorithm
  • 2. Advantages of Reinforcement Learning Algorithm
  • 3. Components of Reinforcement Learning Algorithm
  • 4. Exploration Vs Exploitation tradeoff

Why Choose MAK Technologies?

Industry-Oriented Training

Real-Time Project Learning

Flexible Online Classes

Expert Mentor Support

Why You Should Learn Data Science ?

  • High demand for jobs in many industries.
  • Data-related roles help you grow in your career.
  • Useful in IT, finance, healthcare, and marketing
  • Helps businesses make better decisions.
  • Combines thinking, problem-solving, and analyzing skills.
  • Combines thinking, problem-solving, and analyzing skills.
  • Learn from real examples and case studies.
  • Continuous learning and skill improvement.

$150.99

  • Class Online
  • Language English, Tamil, Hindi
  • Certificate Yes
  • Level Beginner
  • Topic Data Science
    • Other includes:
    • ✓ Class recordings
    • ✓ Online certification
    • ✓ Online classes conducted Google Meet and Zoom
    • ✓ Online interviews
    • ✓ Foreign countries study guidance available
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Career Benefits of Learning Data Science with Us

Frequently Asked Questions (FAQ)

Students, graduates, working professionals, and career switchers can join this course.

Basic programming knowledge is helpful but not mandatory. The course starts from fundamentals.

Data Analyst, Data Scientist, Business Analyst, Machine Learning Engineer, or Analytics Consultant.

Yes. With proper training, learners from non-IT backgrounds can transition into data roles.

Yes. The course includes hands-on projects using real industry datasets.

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