– Intro to Python

– Variables, Data Types & Type Conversion in Python

– Numbers & Strings in Python

– Working with Lists in Python

– Conditionals & Loops in Python

– Functions & Built in Functions in Python

– NumPy in Python

– Dictionaries in Python

– Pandas Library in Python

– Manipulating Data Frames

– Data Visualization in Python (Matplotlib & Seaborn)

– Data Wrangling

– Data pre-processing and scaling

– Feature Extraction

– Measures of central tendency

– Measure of dispersion

– Probability distribution

– Data visualization techniques & charts

– Data Pre-processing and scalling

– Data wrangling

– Feature extraction

– Splitting the dataset as per model requirement

– An introduction to time-series and stationary data

– Key concepts that include filters, signal transformations, and anomalies

– Spot trends, seasonality and secrets hidden in your data

– How to handle and visualize time series data

– Learn tricks to forecast what’s next.

– The application of time series forecasting with Python

– K-nearest neighbors Naive Bayes

– Linear Regression

– Multivariable Regression

– Logistic Regression

– Decision Tree 

– Support Vector Machine

– K-fold methods

– Model evaluation techniques

– Career Prospects in Data Science, Data Analytics, Business Intelligence, and Analytical skills for Sales, marketing, Supply Chain, Finance and HR and other professionals

– Business case studies & capstone projects in each module

– Special guest sessions from Industry leaders from Amazon, Microsoft, Nike, Jazz and more.

 – Cracking interviews with skills & tricks