Data Analysis - 60 Hours

45%

Course Description

Data Science, Machine Learning, Deep Learning and Artificial Intelligence

Objectives of the Program

•  To enable participants understand how Data Science is used in every aspect of our daily lives and businesses
•  To enable the participants to learn basics of Python.
•  To enable the participants to learn Data Science/Machine learning/Deep Learning using Python.
•  To empower the participants with sufficient knowledge to use latest technologies of Data Science (Hands-On).
•  To make the participants understand overview of how Exploratory Data Analysis works.
•  To provide the participants Hands-on experience in Data science concepts.
•  To provide the participants Hands-on experience in Machine Learning concepts.
•  To empower the participants with sufficient knowledge to use latest technologies of Data Science/Machine Learning/Deep Learning/ Artificial Intelligence (Hands-On).
•  Overview of the Program Content
•  Introduction to Data Science, Machine Learning, Deep Learning and Artificial Intelligence
•  Basics of Python and Hands-on on Python
•  Introduction to Data Science using Python
•  Hands on session on Data Science with Python [Database Connectivity with PYTHON: Performing Database Transactions (Inserting, Deleting, and Updating the   Database)]
•  Introduction to Machine Learning and its types
•  Examples Overview of Machine Learning packages in Python
•  Introduction to Deep Learning and its types
•  Project in Data Science/Machine Learning /Artificial Intelligence

CURRICULUM DETAILS: MODULES AND CHAPTERS

Module 1: Python for Data Science/Machine Learning/Deep Learning

•  Basic building blocks of python
•  Data Types
•  Operators
•  Control Structure
•  Functions
•  Data Structures
•  String Operations and Regular Expressions
•  File Handling
•  Exception Handling
•  The Object-Oriented Approach

Module 2: Maths Behind Data Science

•  Statistical Analysis: Descriptive Statistics & Inferential Statistics
•  Hypothesis Testing

Module 3: Data Science (Exploratory data analysis (EDA) /Data wrangling/
Data Munging/Data Cleaning Techniques/ Data Preparation/Feature
Engineering)

o Data Science Library and data visualization Using Python
o Data Exploration (Univariate/Bivariate/Multivariate Analysis)
o Missing value treatment
o Outlier treatment
o Categorical to Numerical
o Feature Rescaling
o Feature (Variable) transformations
o Correlation Analysis
o Feature selection Methods

Module 4: Data Visualization using Tableau

Tools, Languages and Libraries Used:

1. Sql
2. Python
3. Anaconda
4. Jupyter Notebook
5. Google Colab
6. Tableau
7. Numpy
8. Pandas

9. Matplotlib
10. Seaborn
11. Sklearn
12. Scipy
13. Tensorflow
14. Keras