Objective

This Short-Term program in Data Science Foundation & Machine Learning empowers learners to analyze data with industry-standard tools and techniques. The mentors take an efficient approach for teaching analytics, enabling learners to immediately apply their knowledge to solve business problems, internships, and live projects.Throughout the program, learners will strengthen their knowledge with practice exercises, assignments, and live projects.

Highlights

Flip classroom (Combination of recordings & Live sessions)
Pre-recorded videos explaining the concepts
Live Mentored sessions for hands-on practice with python notebooks
Domain centric problem-solving exercises
Focus on the 5 W’s (Why, Who, What, Where, When) while applying the concepts on the case studies.
Industry Sessions
Use of Git and GitHub for project repository
Module-wise Tests and Projects

5 Months & 88 Hours

No Technical Background Required

4 Course Projects and 1 Capstone Project

Ideal for Students, Working Professionals, Executives

Cost - 35,000 plus GST

Personalized Mentoring with small groups of 8-10 learners.

Avail 20% Discount

Bring a friend and get 20% discount on your programme fees.

OR

Show your student ID and get 20% discount on your Programme fees.

Course Content

Participants will take approximately 5 months to complete this program. Following are the modules covered under this program

Introduction to Python

2 Weeks
  •    Python Basics
  •    Python Functions and Packages
  •    Datatypes, Functions
  •    List, Tuple, Dictionary, Flow Control
  •    NumPy and Pandas
  •    Matplotlib and Seaborn

Statistical Base for Data Science

2 weeks
  •   Descriptive Statistics (EDA)
  •   Probability & Conditional Probability
  •   Hypothesis Testing
  •   Inferential Statistics
  •   Probability Distributions (EDA)

Unsupervised learning

2 weeks
  •   K-means Clustering
  •   Hierarchical Clustering
  •   Dimension Reduction-PCA
  •   Market Basket Analysis

Supervised learning

2 weeks
  •    Linear Regression
  •    Logistic Regression
  •    Feature Engineering
  •    KNN Classification

Ensemble Techniques

2 weeks
  •    Decision Trees
  •    Random Forest
  •    Gradient Descent
  •    Boosting/XGBoost

Model Selection & Tuning

1 week
  •   Feature Engineering
  •   Hyperparameter Tuning
  •   Model Performance Measures
  •   Cross Validation Techniques

Time-Series Forecasting

1.5 weeks
  •    Naive and Moving Average Forecasting
  •    ARIMA Approach
  •    Exponential Smoothing
  •    FbProphet

Data Visualization

1.5 weeks
  •   Data Exploration with Tableau
  •   Forecasting with Tableau
  •   Creating Tableau Storyboards

Course Projects

4 weeks
  •    Statistical base for Data Science
  •    Unsupervised Learning
  •    Supervised Learning
  •    Machine Learning E2E Case Study

Capstone Project

4  weeks
  •    Retail
  •    Banking
  •    Insurance
  •    E-Commerce
  •    Healthcare

Tools & Techniques

FbProphet
Python
Anaconda
Scikit Learn
Matplot Lib
Numpy
Pandas
Tableau
StreamLit
Seaborn
Stats Model
Knime

Contact Us

9892358595
nextlearningstep@gmail.com
Give us a call or drop by anytime, we endeavour to answer all enquiries within 24 hours on business days.