What you learn in Data Science Course?

• Introduction to Data Science

The Concept of Data science and its needs
Business Intelligence Versus Data Science
Data Analytics Versus Data Science
The Knowledge domains of a data scientist
Data science lifecycle with the help of a use case
The Nature of the Data: Data objects and attribute types
Various data formats: CSV,Flat Files,SQL,noSQL etc.
Tools Available to Data Scientists
Data-Driven Decision Making
Who have to make Data-Driven Decisions ?


• Preparing Data Science Environment

Anaconda Python Distribution and its installation
Using R and R studio and its installation
Benifits of Unix Shell and how to use it
Advantages of using Git and its installation
Installing R on Windows and Linux
Installing libraries in R and R studio
Installing Python on Windows and Linux
Installing the Python Data stack on Linux
Installing extra Python packages


• Python for Data Science

Introduction to Python and IPython
Installation of Python framework and Packages:Anaconda
Introduction to Jupyper Notebook, Python IDE and Spider
Essential Python Packages for Data SCience
Variables and Data types in Python
Python Operators and Expressions
Sequence Types:List,Tuples and Strings
Range,Sets and Dictionaries
Control Structures and Functions
Classes and Object-oriented Programming
Errors and Exception Handling
Modules and Packages


• Mathematics for Data Science

NumPy Basics
NumPy for matrices & vectors and their mathematical operations
Vectors
Matrices
Least Squares: Linear Regression
Vector Gradient Descent
Understanding the applications of linear algebra in data science
Probability


• Data Extraction & Web Scrapping

Introduction to Python Packages
Pandas Data Structure
Delve into data analysis and manipulation using pandas
Importing and Exporting Data
Web Scraping: Acquiring and Storing Data from the Web


• Data Wranging: Data Scrubbing/ Data Cleansing

Filling in the missing values for data attributes
Reduce redundancy by de-duplicating the data
Reduce the noisy data by identifying the outliers
Modify the string field column in the data with string operations


• Data Wranging: Data Integration & Reduction

Integrating data using pandas merge and join
Combining data from different sources using both NumPy and Pandas: concat and append
Handling the case where input dataFrames has conflicting column names
reshaping with hierarchical indexing and pivoting in pandas
performing hierarchical indexing to represent higher-dimensional data compactly within the one-dimensional Series and two-dimensional DataFrame objects.


• Data Wranging: Data Aggregation & String Manipulation

Groupby interface to slice,dice, and summarize datasets
Spliting a pandas object into pieces using one or more keys
Pivot tables and cross-tabulations
Quantile analysis and other statistical group analysis
Strings and text processing with pandas bult-in functions and regular expression


• Exploratory Data Analysis(EDA)

Data Summarization with Descriptive Statistics
EDA with Data visualization


• Inferential Statistics

Installing Python Packages
Sampling and Sampling Distributions
Estimation Theory
Hypothesis Testing
Nonparametric tests
Experimental Designs


• Statistical Modeling for Data Science

Introduction to Statistical Modeling
Statistical Models
Prediction with linear and logistic regressions
Multiple Regressions
Using generalized additive models(GAMs)
Evaluation metrics for Regression


• Machine Learning Modeling for Data Science

Why Machine Learning?
Problems Machine Learning can Solve
Types of Machine Learning
Essential Python Libraries and Tools for Data Science
Machine Learning Modeling


• Feature Selection & Dimensionality Reduction

Introduction to feature selection and dimensionality reduction
Feature selection with scikit-learn


• Supervised Machine Learning
• Un-Supervised Machine Learning
• Deep Learning using Tensorflow
• Data visualization using Matplotlib and Tableau
• Databases and SQL
• Handling Big data with Spark
• Natural Language Processing
• Social Network Analysis
• Recommendation Systems