Data Science with Python

About Course

Data Science and analysis is the wave which is transforming every business model and decision-making capability of leaders, marketers, thinkers, strategists and business analysts. Learn to handle the bulk amount of data and using data for growth and efficiency of the organization.

We at TechTrunk Ventures provide you extensive Data Science course which not only covers the advanced tools like Python programming, python libraries like numpy, scipy, matplotlib, pandas, scikit-learn, hive, Hadoop, HDFS, Scala but it also covers the backend mathematics behind machine learning and data prediction techniques. We make you a complete mathematician with Data Analytics skills because we strongly believe that –

  “Mathematics is inevitable”

Learn Data Acquisition, Data wrangling, Data Exploration, Data handling, prediction using Data and Data Visualization.

Fee: INR 15000/-

Duration: 45 hours

Mode of Training: Online Live Class

Upcoming Batch:

  • Online Batch: 10th August-7:00 PM – 10:00 PM, IST,  (Mon-Fri)

Course Content

  • Lesson 1: Data Science Overview

    • Data Science
    • Data Scientists
    • Examples of Data Science
    • Python for Data Science

     

    Lesson 2: Data Analytics Overview

    • Introduction to Data Visualization
    • Processes in Data Science
    • Data Wrangling, Data Exploration, and Model Selection
    • Exploratory Data Analysis or EDA
    • Data Visualization
    • Plotting
    • Hypothesis Building and Testing

  • Lesson 3: Statistical Analysis and Business Applications

    • Introduction to Statistics
    • Statistical and Non-Statistical Analysis
    • Some Common Terms Used in Statistics
    • Data Distribution: Central Tendency, Percentiles, Dispersion
    • Histogram
    • Bell Curve
    • Hypothesis Testing
    • Chi-Square Test
    • Correlation Matrix
    • Inferential Statistics

     

    Lesson 4: Python: Environment Setup and Essentials

    • Introduction to Anaconda
    • Installation of Anaconda Python Distribution – For Windows, Mac OS, and Linux
    • Jupyter Notebook Installation
    • Jupyter Notebook Introduction
    • Variable Assignment
    • Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
    • Creating, accessing, and slicing tuples
    • Creating, accessing, and slicing lists
    • Creating, viewing, accessing, and modifying dicts
    • Creating and using operations on sets
    • Basic Operators: ‘in’, ‘+’, ‘*’
    • Functions
    • Control Flow

  • Lesson 5: Mathematical Computing with Python (NumPy)

    • NumPy Overview
    • Properties, Purpose, and Types of ndarray
    • Class and Attributes of ndarray Object
    • Basic Operations: Concept and Examples
    • Accessing Array Elements: Indexing, Slicing, Iteration, Indexing with Boolean Arrays
    • Copy and Views
    • Universal Functions (ufunc)
    • Shape Manipulation
    • Broadcasting
    • Linear Algebra

     

    Lesson 6: Scientific computing with Python (Scipy)

    • SciPy and its Characteristics
    • SciPy sub-packages
    • SciPy sub-packages –Integration
    • SciPy sub-packages – Optimize
    • Linear Algebra
    • SciPy sub-packages – Statistics
    • SciPy sub-packages – Weave
    • SciPy sub-packages – I O

  •  

    Lesson 7: Data Manipulation with Python (Pandas)

    • Introduction to Pandas
    • Data Structures
    • Series
    • DataFrame
    • Missing Values
    • Data Operations
    • Data Standardization
    • Pandas File Read and Write Support
    • SQL Operation

     

    Lesson 8: Machine Learning with Python (Scikit–Learn)

    • Introduction to Machine Learning
    • Machine Learning Approach
    • How Supervised and Unsupervised Learning Models Work
    • Scikit-Learn
    • Supervised Learning Models – Linear Regression
    • Supervised Learning Models: Logistic Regression
    • K Nearest Neighbors (K-NN) Model
    • Unsupervised Learning Models: Clustering
    • Unsupervised Learning Models: Dimensionality Reduction
    • Pipeline
    • Model Persistence
    • Model Evaluation – Metric Functions

  • Lesson 9: Natural Language Processing with Scikit-Learn

    • NLP Overview
    • NLP Approach for Text Data
    • NLP Environment Setup
    • NLP Sentence analysis
    • NLP Applications
    • Major NLP Libraries
    • Scikit-Learn Approach
    • Scikit – Learn Approach Built – in Modules
    • Scikit – Learn Approach Feature Extraction
    • Bag of Words
    • Extraction Considerations
    • Scikit – Learn Approach Model Training
    • Scikit – Learn Grid Search and Multiple Parameters
    • Pipeline

  • Lesson 10: Data Visualization in Python using Matplotlib

    • Introduction to Data Visualization
    • Python Libraries
    • Plots
    • Matplotlib Features:
    • Line Properties Plot with (x, y)
    • Controlling Line Patterns and Colors
    • Set Axis, Labels, and Legend Properties
    • Alpha and Annotation
    • Multiple Plots
    • Subplots
    • Types of Plots and Seaborn

  • Lesson 11: Data Science with Python Web Scraping

    • Web Scraping
    • Common Data/Page Formats on The Web
    • The Parser
    • Importance of Objects
    • Understanding the Tree
    • Searching the Tree
    • Navigating options
    • Modifying the Tree
    • Parsing Only Part of the Document
    • Printing and Formatting
    • Encoding

     

    Lesson 12: Python integration with Hadoop, MapReduce and Spark

    • Need for Integrating Python with Hadoop
    • Big Data Hadoop Architecture
    • MapReduce
    • Cloudera QuickStart VM Set Up
    • Apache Spark
    • Resilient Distributed Systems (RDD)
    • PySpark
    • Spark Tools
    • PySpark Integration with Jupyter Notebook

 

 

 

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