The “Data Science” course is an intermediate level course, curated exclusively for both beginners and professionals.

The course covers the basics as well as the advanced level concepts. The course contains content based videos along with practical demonstrations, that performs and explains each step required to complete the task.

Learning Objectives:

By the end of the course, you will be able to learn about:

• Data Science in detail

• Sectors Using Data Science

• Purpose and Components of Python

• Data Analytics Process

• Exploratory Data Analysis (EDA)

• EDA-Quantitative Technique

• EDA - Graphical Technique

• Data Analytics Conclusion or Predictions

• Data Analytics Communication

• Data Types for Plotting

• Data Types and Plotting

• Introduction to Statistics

• Statistical and Non-statistical Analysis

• Major Categories of Statistics

• Statistical Analysis Considerations

• Population and Sample

• Statistical Analysis Process

• Data Distribution

• Dispersion

• Histogram

• Testing

• Correlation and Inferential Statistics

• Anaconda

• Installation of Anaconda Python Distribution

• Data Types with Python

• Basic Operators and Functions

• Numpy

• Creating and Printing an ndarray

• Class and Attributes of ndarray

• Basic Operations

• Activity-Slice It

• Copy and Views

• Mathematical Functions of Numpy

• Analyzing London Olympics Dataset

• Introduction to SciPy

• SciPy Sub Package - Integration and Optimization

• SciPy sub package

• Calculating Eigenvalues and Eigenvector

• Identifying the SciPy Sub Package

• Solving Linear Algebra problem using SciPy

• Performing CDF and PDF using Scipy

• Introduction to Pandas

• Understanding DataFrame

• View and Select Data

• Missing Values

• Data Operations

• File Read and Write Support

• Pandas SQLOperation

• Analyzing NewYork city fire department Dataset

• Introduction to Machine Learning Approach

• How it Works?

• Supervised Learning Model Considerations

• Supervised Learning Models - Linear Regression

• Supervised Learning Models - Logistic Regression

• Introduction to Unsupervised Learning Models

• Pipeline

• Model Persistence and Evaluation

• Building a model to predict Diabetes

• Introduction to NLP

• Applications of NLP

• NLP Libraries-Scikit

• Extraction Considerations

• Scikit Learn-Model Training and Grid Search

• Sentiment Analysis using NLP

• Introduction to Data Visualization

• Line Properties

• (x,y) Plot and Subplots

• Types of Plots

• Drawing a pair plot using seaborn library

• Web Scraping and Parsing

• Understanding and Searching the Tree

• Navigating options

• Navigating a Tree

• Modifying the Tree

• Parsing and Printing the Document

• Web Scraping of Any Website

• Identifying the reasons why Big Data Solutions are Provided for Python.

• Components of Hadoop Core

• Python Integration with HDFS using Hadoop Streaming

• Python Integration with Spark using PySpark

• Using PySpark to Determine Word Count

...and much more!

If you're new to this technology, don't worry - the course covers the topics from the basics. If you've done some programming before, you should pick it up quickly.

If you’re a programmer looking to switch into an exciting new career track, this course will teach you the basic techniques used by real-world industry Data Scientist. These are topics any successful technologist absolutely needs to know about, so what are you waiting for? Enroll now!