Become a Certified

Data Scientist

Data Science is the most in-demand job in the market. This course, designed for both students with and without coding experience, makes you a well-rounded data nerd, ready to be hired by top MNCs.

What You’ll Learn

1MODULE 1: PYTHON FOR DATA ANALYSIS

Get a basic overview of Jupyter Notebook and its components; and learn about the three most popular Python libraries: Numpy, Pandas and matplotlib.

  • 1.Environment Set-up
  • 2.Jupyter Overview
  • 3.Python Numpy
  • 4.Python Pandas
  • 5.Python Matplotlib

2MODULE 2: INTRO TO STATISTICS

Statistics is an essential skill required to master data science. This module will introduce you to the most important statistics concepts that are used in data science.

  • 1.Difference between a population and a sample
  • 2.Types of variables
  • 3.Measures of Central Tendency
  • 4.Measures of Variability
  • 5.Coefficient of variance
  • 6.Skewness and Kurtosis

3MODULE 3: : INFERENTIAL STATISTICS

Inferential statistics is a main branch of statistics. It allows you to make informed decisions based on the analysis of a representative sample of large data sets.

  • 1.Normal Distribution
  • 2.Central Limit theorem
  • 3.Confidence Interval
  • 4.Student's T distribution
  • 5.Test hypotheses
  • 6.Type I and Type II errors
  • 7.T-test

4MODULE 4: REGRESSION AND ANOVA

Learn about a powerful statistical method that allows you to establish the relationship between two or more objects of interest. Also learn about analysis of variance (ANOVA), which helps you analyse the diffences among sample sets.

  • 1. Regression
  • 2. Anova
  • 3. Correlation and Causation
  • 4. R square
  • 5.Adjusted R square

5MODULE 5: EXPLORATORY DATA ANALYSIS

Learn how to use visual methods to analyse data sets to summarize their main characteristics. See data beyond formal modeling or hypothesis testing

  • 1. Intro to Exploratory Data Analysis
  • 2. Derive insights of Data with python pandas
  • 3. Missing value analysis
  • 4. Outlier detection analysis
  • 5. The correlation matrix

6MODULE 6: SUPERVISED MACHINE LEARNING

Learn how to use visual methods to analyse data sets to summarize their main characteristics. See data beyond formal modeling or hypothesis testing

  • 1. Intro to Supervised Machine Learning
  • 2. Linear Regression with an example using python sci-kit learn module
  • 3. Logistic Regression with an example using python sci-kit learn module
  • 4. Naïve Bayes method and an example using python sci-kit learn module
  • 5. Decision Tree classifier and an example using python sci-kit learn module
  • 6. Random forest classifier and an example using python sci-kit learn module
  • 7. Support vector machine (SVM) and an example using python sci-kit learn module
  • 8. Intro to Neural network

You'll earn a well-deserved certificate.

Course Highlight

1-to-1 mentoring

We will assign you a mentor to guide you throughout the course and push you towards completing your lessons and projects on time.

Post-Course Support

Get the help to build your resume, do mock interviews and attract job interviews in well-known companies around you.

Project Based Learning

Master the new tech skills through hands-on labs, mini projects and capstone project.