• How to Install Python
    • History of Python
    • Python Variables
    • Loops in Python
    • Python collection Data Types
    • OOPS concepts
    • Exception Handling
    • Regular Expression
    • Python Numpy Arrays
    • Matrix and its operation
    • Functions in Python
    • User Defined functions in Python
    • Scope in Python
    • Introduction to Methods
    • Packages in Python and PIP
    • Pandas and Data frames
    • Import and Export data from CSV
    • Why Data Preprocessing
    • Missing Values Treatment
    • Encoding
    • Feature Scaling
    • Outlier Treatment
    • Template for Data Preprocessing
  • Introduction to Matplotlib and Seaborn
  • Various charts and syntax
    • Introduction to Machine Learning
    • Types of Machine Learning
    • Basic Probability required for Machine Learning
    • Linear Algebra required for Machine Learning
  • Simple Linear Regression
    • Simple Linear Regression Intuition
    • Simple Linear Regression – Business Problems
  • Multiple Linear Regression
    • Multiple Linear Regression Intuition
    • Multiple Linear Regression – Business Problems
  • Polynomial Regression
    • Polynomial Regression Intuition
    • Polynomial Regression Business problem
    • Implementation
  • Decision Tree
    • Decision Tree Intuition
    • Decision Tree Business problem
    • Implementation
  • Random Forest
    • Random Forest Intuition
    • Random Forest Business problem
    • Implementation
    • Logistic Regression
    • Decision Trees
    • Random Forests
    • SVM
    • Naïve Bayes
    • KNN
    • Confusion Matrix
    • Types of K-Means
    • K-Means Clustering
    • Hierarchal Clustering
    • Principal Component Analysis
    • Linear Discriminant Analysis
    • Need for recommendation engines
    • Types of Recommendation Engines
    • Content-Based
    • Collaborative Filtering
    • Apriori Algorithm
    • Market Basket Analysis
    • Understanding Time Series Data
    • ARIMA analysis
    • Statistics – Descriptive Statistics
    • Statistics – Inferential Statistics Fundamentals
    • Statistics – Hypothesis Testing
    • Summary of the key learning of the course.
  • There will be an online training followed by a multiple choice exam of 100 marks.
  • You need to acquire 60+ marks to clear the exam.
  • If you fail, you can retake the exam after one day.
  • You can take the exam no more than 3 times.
  • If you fail to acquire 60+ marks even after three attempts, then you need to contact us to get assistance for clearing the exam

Certification Benefits

  • Opens the world of excellent career opportunities in data sciences domain
  • Certification in data sciences ramps up your career growth
  • Get highly paid with data science expert certification

What you get?

  • Global Tech Council Certification
  • Career guidance in big data analytics domain
  • Peer-to-Peer networking opportunity
  • 1 to 1 counselling with our career experts


Top job functions

  • Business Development
  • Engineering
  • Information Technology
  • Operations
  • Sales

What does a Data Science Developer do?

Data Science Developer works closely with data, the certified individual knows how to extract and interpret data.

The Growth Curve ahead:

  • Business Analyst
  • Business Intelligence Analyst
  • Data Scientist

Final Outcome

After completing this certification, you will master the core concepts of Data Science.

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Success Stories