 Data Science
 9 (Registered)

Detailed Coverage:
 Data Science Overview
 Objectives:Know what Data Science is, Discuss the roles and responsibilities of a Data Scientist, List various applications of Data Science, Understand how Data Science and Big Data work together, Explore Data Science as a discipline, Understand how and why Data Science is gaining importance and Understand what Python is and what problems it resolves.
 Introduction to Data Science
 Different Sectors Using Data Science
 Purpose and Components of Python
 Data Analytics Overview
 Objectives:Data Analytics process and its steps, Skills and tools required for Data Analysis, Challenges of the Data Analytics Process, Exploratory Data Analysis technique, Data visualization techniques and Hypothesis testing to analyze data.
 Data Analytics Process
 Exploratory Data Analysis(EDA)
 EDAQuantitative Technique
 EDA Graphical Technique
 Data Analytics Conclusion or Predictions
 Data Analytics Communication
 Data Types for Plotting
 Data Types and Plotting
 Statistical Analysis and Business Applications
 Objectives: The difference between statistical and nonstatistical analysis, The two major categories of statistical analysis and their differences, The statistical analysis process, Mean, median, mode, and percentile, Data distribution and the various methods of representing it, Hypothesis testing and the Chi square test, Types of frequencies and Correlation matrix and its uses.
 Introduction to Statistics
 Statistical and Nonstatistical Analysis
 Major Categories of Statistics
 Statistical Analysis Considerations
 Population and Sample
 Statistical Analysis Process
 Data Distribution
 Dispersion
 Histogram
 Testing
 Correlation and Inferential Statistics
 Python: Environment Setup and Essentials
 Objectives: The difference between statistical and nonstatistical analysis, The two major categories of statistical analysis and their differences, The statistical analysis process, Mean, median, mode, and percentile, Data distribution and the various methods of representing it, Hypothesis testing and the Chi square test, Types of frequencies and Correlation matrix and its uses.
 Anaconda
 Data Types with Python
 Basic Operators and Functions
 Mathematical Computing with Python (Numpy)
 Objectives:What NumPy is and why it is important, Basics of NumPy, including its fundamental objects, Create and print a NumPy array, Carry out basic operations in NumPy, Use shape manipulation and copying methods, Execute linear algebraic functions and Build basic programs using NumPy
 Introduction to Numpy
 Activity Sequence it Right
 Class and Attributes of ndarray
 Basic Operations
 Copy and Views
 Mathematical Functions of Numpy
 Scientific Computing with Python (Scipy)
 Objectives: Why SciPy is needed, The characteristics of SciPy, The subpackages of SciPy and SciPy Subpackages such as Optimization, Integration, Linear Algebra, Statistics, Weave, and IO.
 Introduction to SciPy
 SciPy Sub Package – Integration and Optimization
 SciPy sub package
 Demo – Calculate Eigenvalues and Eigenvector
 SciPy Sub Package – Statistics, Weave and IO
 Data Manipulation with Pandas
 Objectives: Pandas and its features, Different data structures of Pandas, Creating Series and DataFrame with data inputs, Viewing, selecting, and accessing elements in a data structure, Handling vectorized operations, Learning how to handle missing values and Analyzing data with different data operation methods.
 Introduction to Pandas
 Understanding DataFrame
 View and Select Data Demo
 Missing Values
 Data Operations
 File Read and Write Support
 Activity Sequence it Right
 Pandas Sql Operation
 Machine Learning with Scikit–Learn
 Objectives:What machine learning is and why it is important, The machine learning approach, Relevant terminologies that help you understand a dataset, Features of supervised and unsupervised learning models and Algorithms such as regression, classification, clustering, and dimensionality reduction.
 Machine Learning Approach
 How it Works
 Supervised Learning Model Considerations
 ScikitLearn
 Supervised Learning Models
 Unsupervised Learning Models
 Pipeline
 Model Persistence and Evaluation
 Natural Language Processing (NLP) with ScikitLearn
 Objectives What is Natural Language Processing, How Natural Language Processing is helpful, Modules to load content and category, Applying feature extraction techniques and Applying approaches of Natural Language Processing.
 NLP Overview
 NLP Applications
 NLP LibrariesScikit
 Extraction Considerations
 Scikit LearnModel Training and Grid Search
 Data Visualization in Python using Matplotlib
 Objectives:Explain what data visualization is and its importance in our world today, Understand why Python is considered one of the best data visualization tools,Describe matplotlib and its data visualization features in Python and List the types of plots and the steps involved in creating these plots.
 Introduction to Data Visualization
 Line Properties
 (x,y) Plot and Subplots
 Types of Plots
 Web Scraping
 Objectives:Define web scraping and explain the importance of web scraping, Lists the steps involved in the web scraping process, Describe basic terminologies such as parser, object, and tree associated with the BeautifulSoup and Understand various operations such as searching, modifying, and navigating the tree to yield the required result.
 Web Scraping and Parsing
 Understanding and Searching the Tree
 Navigating options
 Demo Navigating a Tree
 Modifying the Tree
 Parsing and Printing the Document
Course Content
Curriculum is empty

The data science course is wellstructured and the course material eAge offers is superb. The mentor was incredibly helpful and the realtime data science projects build my confidence in cracking the job interview

Great place to start learning. So many useful references. The overall experience was very satisfying.