Business Analytics

Covers Python (Internals, do’s and don’ts) Architecture, Data Structure, Python, Reading & Writing files in Python and Loops and conditionals in python and more.

Data and AI · Live online sessions · 14 modules · Certificate

What you will learn

  1. Python (Internals, do’s and don’ts) Architecture, Data Structure

    • Installation of Anaconda Prompt
    • Jupyter Notebook-An Overview
    • Shorcut Lkeys in Jupyter Notebook
    • Data Types in Python

  2. Python, Reading & Writing files in Python

    • Rules for Naming the Variables
    • List, Tuple, Set, Dictionary
    • Introduction to Files and Directories
    • Introduction to the Command Prompt or Terminal Paths
    • Text Files: Reading from a Text File (using with)

  3. Loops and conditionals in python

    • If, Elif, and Else Conditions
    • For and While Loops

  4. Data Analysis , Manipulation with numpy

    • Machine Learning Libraries
    • Numpy-Hands on

  5. Pandas Python data science package to manipulate, calculate and analyze data.

    • "Pandas-Hands on"

  6. Exploratory Data Visualization in Python with matplotlib

    • Exploring & Extracting Insights from Data
    • Data Visualization Basics
    • Matplotlib – Hands-on
    • Seaborn – Hands-on

  7. Statistical Thinking in Python (Part 1) Build the foundation

    • Thinking Statistically – Introduction
    • Measures of Central Tendency
    • Measures of Dispersion
    • IQR Statistics – Hands-On

  8. Supervised Learning & Un-Supervised Learning

    • Classification vs Regression
    • Supervised vs Unsupervised Learning
    • Linear Regression – Hands-on
    • Metrics in Linear Regression – Hands-on
    • Fine-Tuning Your Model

  9. Logistic regression

    • Introduction to Logistic Regression
    • Hands-on with Logistic Regression
    • Metrics in Logistic Regression

  10. Linear Regression

    • Introduction to Linear Regression
    • Hands-On with Linear Regression using Python
    • Metrics in Linear Regression

  11. Pre-processing for Machine Learning in Python

    • Introduction to Data Preprocessing
    • Exploratory Data Analysis (EDA)
    • Missing Values
    • Outliers
    • Standardizing Data / Scaling Techniques
    • Feature Scaling and Feature Selection

  12. Tree Based Models Classification and Regression Tree

    • Decision Tree
    • Boosting Random Forest
    • Bagging

  13. Fundamentals of Neural Network

    • Neural Network

  14. POWER BI

    • Introduction to Power BI desktop
    • ETL pipeline in Power BI
    • Calculating fields with DAX
    • Visualising data with reports
    • AI functionalities of Power BI

Download the Business Analytics brochure (PDF)