Data Science Certification Training Course Chennai | Best Online Learning Courses Chennai

Data Science Certification Training Course

Start your journey to become a Professional Python Programmer

Mode of delivery: Hybrid/ Live Online/ Self Paced/ Classroom Sessions

Duration: 6 Months

200+ Hiring Partners

Zero percent EMI Option available

Unique highlights of the programme

In this practical, hands-on course, the main objective is to help students understand the ins and outs of data science. This online training course takes you from the basics to mastery and will equip you with the skills required to create software programs, scrape websites, data extraction, and build automation.

Module 1 - Data Science Fundamentals

Curriculum
Data Science Overview
Define Data Science Data Science Importance Python and Its importance Roles and responsibilities of Data Scientist Various Applications of Data Science
Data Analytics Overview
Data Analytics Process and Its steps Skills and Tools Required for Data Analysis Challenges in Data Analytic Processes Data Visualization Technique Exploratory Data Analysis Technique Hypothesis Testing

Module 2 - Statistical Analysis and Business Applications

Curriculum
Statistical vs Non-Statistical Analysis Major categories of Statistics Population-Sample Statistical Analysis Process
Distribution Bell Curve Hypothesis Testing
Data Types and Variable types Types of Frequencies Chi- Square test
Correlation matrix Inferential Statistics Use of range() function

Module 3 - Numeric & Scientific Computing with Python

Curriculum
Numeric Computing with Python
Difference between Numpy and Scipy Package Numpy Fundamentals
Numpy DataTypes
Working with Arrays
Boolean and fancy Indexing
Generating Data With Numpy
Scientific Computing with Python
SciPy and its characteristics ScipPy Sub-Package Sub-package Optimization Sub-package Linear Algebra
Sub-package Statistics Sub-package Weave and IO Sample Programs

Module 4 - Data Manipulation, Getting & Cleaning Data

Curriculum
Data Manipulation
Pandas and its Features Data Structures
Series
Dataframes
Data Operations
Data Standardization Pandas SQL Operations
Getting Data
Understanding the domain and the dataset Importing and Exporting Data Basic Insights from dataset
Cleaning Data
Missing values and Formatting data
Normalization, Indicator Variables and Binning

Module 5 - Encapsulating the data & Development of Model

Curriculum
Encapsulating the data
Descriptive Statistics Grouping
ANOVA Correlation
Development of Model
Simple and Multiple Linear Regression
R-squared and MSE for sample Evaluation
Polynomial Regression nd Pipelines
Prediction and Decision Making

Module 6 - Evaluation of Model

Curriculum
Model Evaluation Over-fitting and Under-fitting
Model Selection Ridge Regression
Grid Search Refining the model

Module 7 - Data Visualization & Types of Plots

Curriculum
Data Visualization in Python using Matplotlib
Data Visualization Considerations of Data Visualization Factors of Data Visualization Python libraries for Data Visualization
Steps to create a plot Line Properties Alpha and Annotation Multiple Plots
Types of Plots
Seaborn module Pandas built in functions for data visualization
Basic Visualization Tools
Specialized Visualization Tools
Advanced Visualization Tools
Plotly and Cufflinks
Maps and Geospatial Data
Netflix Project Discussion

Module 8 - Hadoop & Apache Spark

Curriculum
Python Integration with Hadoop Map Reduce and Spark
Integrating python with Hadoop Hadoop Components and system architecture
Map Reduce Cloudera Quickstart Virtual Machine
VMware Image Word Count Program
Apache Spark
RDD Advantages of Spark Python API for Spark
Spark Tools Setting up Apache Spark

Module 9 - Web Scraping & Natural Language Processing

Curriculum
Web Scraping with Beautiful Soup
Web Scraping and its advantages Web Scraping process Beautiful Soup and its Features
Parser Tree
Searching Tree Navigating Options
Output : Printing and Formatting Encoding
Natural Language Processing with Scikit Learn
NLP – Benefits, Applications Modules to Load content and category
Implementation of Extraction
Feature Extraction Techniques

Module 10 - Regression Cluster Analysis

Curriculum
Regression
Linear regression
Logistic regression
Lasso regression
Bayesian Linear regression
Cluster Analysis
Introduction K- means
Hierarchial GMM

Module 11 - Principal Component Analysis & Recommender Systems

Curriculum
Principal Component Analysis
Introduction Eigen values and Vectors
Applications Implementation
Recommender Systems
Simple Recommender
Content Based Recommender
Colloborative Filtering Engines

Module 12 - Neural Nets and Deep Learning

Curriculum
ANN Neural Networks
TensorFlow
Keras

MODULE – 13 Supervised & Unsupervised Learning

Curriculum
Supervised Learning
Classification Problems
Regression
Fine-tuning the model Preprocessing and Pipelines
Unsupervised Learning
Clustering data Visualization
Decorerelating and Dimension Reduction Interpretation – NMF
K-means and Hierarchical clustering
Agglomerative and Divisive Hierarchical Clustering
Density Based Clustering
Principal Component Analysis
Singular Value Decomposition
Independent Component Analysis
Analysis with the Real Life Projects