Data Science with Python

Data science with python
12
Mar

Detailed Coverage:

  1. 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
  2. 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)
    • EDA-Quantitative Technique
    • EDA Graphical Technique
    • Data Analytics Conclusion or Predictions
    • Data Analytics Communication
    • Data Types for Plotting
    • Data Types and Plotting
  3. Statistical Analysis and Business Applications
    • Objectives: The difference between statistical and non-statistical 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 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
  4. Python: Environment Setup and Essentials
    • Objectives: The difference between statistical and non-statistical 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
  5. 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
  6. Scientific Computing with Python (Scipy)
    • Objectives: Why SciPy is needed, The characteristics of SciPy, The sub-packages of SciPy and SciPy Sub-packages 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
  7. 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
  8. 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
    • Scikit-Learn
    • Supervised Learning Models
    • Unsupervised Learning Models
    • Pipeline
    • Model Persistence and Evaluation
  9. Natural Language Processing (NLP) with Scikit-Learn
    • 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 Libraries-Scikit
    • Extraction Considerations
    • Scikit Learn-Model Training and Grid Search
  10. 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
  11. 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

 

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Course Content

Time: 3 days

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  • Kanika

    Very well done, the combination of whiteboard lesson and hands on code was really good.

    The data science course is well-structured and the course material eAge offers is superb. The mentor was incredibly helpful and the real-time data science projects build my confidence in cracking the job interview
  • Naren Kumar

    Good training for data scientist course

    Great place to start learning. So many useful references. The overall experience was very satisfying.
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Customized, Immersive, Hands-On Driven

Includes

  • Real time virtual classes
  • Pre course reading material
  • Suppliment resources
  • Language: English
  • Certificate of completion

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Your Dream Course Is Only A Step Away





Your Dream Course Is Only A Step Away