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Data Science With Python

  • 40 Hours

Igmguru’s Python online training course can help you master the ideas and gain in-depth expertise on writing python code and packages like SciPy, Matplotlib, Pandas, Scikit-Learn, NumPy, internet scraping libraries, Lambda function additionally you'll find out how to write down Python code for large knowledge systems like Hadoop & spark.

Key Features

  • Instructor Led Training : 40 Hrs
  • 100% money back guarantee
  • Flexible Schedule
  • 24 X 7 Lifetime Support & Access
  • Experienced Trainers
  • 100% Job Assistance
  • Get Certified & Get Placed

For Individuals

Online Class Room Program

Online Class Room Program

  • 90 days of access to 1+ instructor-led online training classes
  • 180 days of access to high-quality,self-paced learning content designed by experts
  • $230.57

100% Money Back Guarantee ?

Batches start from 27 Oct 2018.


IgmGuru's Python online training course is a complete course which will assist you to obviously perceive the high -level, general dynamic programing language. during this Python programming training, you may be exposed to each the essential and advanced ideas of Python like machine learning, Deep Learning, Hadoop streaming, MapReduce in Python, and work with packages like Scikit and Scipy. There are many advantages to learn python online.

What skills will you learn in this Python Online Training Course?

  • Learn about the fundamentals, significance, and installation of Python
  • Learn about file and sequence operations
  • Learn about MapReduce ideas for Hadoop readying
  • Study OOP, expressions, data types, looping, etc.
  • Understand SQLite in Python, functions, operations and sophistication process
  • Use Python for writing and deploying Pig UDF and Hive UDF
  • Get to understand the Machine Learning Algorithms in Python
  • Work on a true life Hadoop project running on Python

Who should opt for this training course?

Following IT professionals can join the Python Online Training Course:

  • BI Managers and Project Managers
  • Software Developers and ETL professionals
  • Analytics professionals
  • Big Data professionals
  • Those wanting to have a career in Python

What are the prerequisites for learning Python?

You don’t need any specific knowledge to learn Python. A basic knowledge of programming can help

Why Should I learn Python Programming Language?

Python is an extremely fashionable object-oriented language that's quick to be told and straightforward to deploy. It will run on varied systems like Windows, UNIX system, and mac so build it extremely desirable for the info analytics domain. Upon completion of Python certification training, you'll be able to add the large knowledge Hadoop surroundings for terribly high salaries.

Python’s design & libraries provide 10 times productivity compared to C, C++, or Java

A Senior Python Developer in the United States can earn $102,000 –


Lesson 1 - Data Science Overview

  • 1.1 Data Science
  • 1.2 Data Scientists
  • 1.3 Examples of Data Science
  • 1.4 Python for Data Science

Lesson 2 - Data Analytics Overview

  • 2.1 Introduction to Data Visualization
  • 2.2 Processes in Data Science
  • 2.3 Data Wrangling, Data Exploration, and Model Selection
  • 2.4 Exploratory Data Analysis or EDA
  • 2.5 Data Visualization
  • 2.6 Plotting
  • 2.7 Hypothesis Building and Testing

Lesson 3 - Statistical Analysis and Business Applications

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

Lesson 4 - Python: Environment Setup and Essentials

  • 4.1 Introduction to Anaconda
  • 4.2 Installation of Anaconda Python Distribution - For Windows, Mac OS, and Linux
  • 4.3 Jupyter Notebook Installation  Jupyter Notebook Introduction
  • 4.4 Variable Assignment
  • 4.5 Basic Data Types: Integer, Float, String, None, and Boolean; Typecasting
  • 4.6 Creating, accessing, and slicing tuples
  • 4.7 Creating, accessing, and slicing lists
  • 4.8 Creating, viewing, accessing, and modifying dicts
  • 4.9 Creating and using operations on sets
  • 4.10 Basic Operators: 'in', '+', '*'  Functions  Control Flow

Lesson 5 - Mathematical Computing with Python (NumPy)

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

Lesson 6 - Scientific computing with Python (Scipy)

  • 6.1 SciPy and its Characteristics  SciPy sub-packages
  • 6.2 SciPy sub-packages –Integration  SciPy sub-packages – Optimize
  • 6.3 Linear Algebra
  • 6.4 SciPy sub-packages – Statistics  SciPy sub-packages – Weave
  • 6.5 SciPy sub-packages - I O

Lesson 7 - Data Manipulation with Python (Pandas)

  • 7.1 Introduction to Pandas
  • 7.2 Data Structures  Series  DataFrame  Missing Values
  • 7.3 Data Operations  Data Standardization
  • 7.4 Pandas File Read and Write Support
  • 7.5 SQL Operation

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

  • 8.1 Introduction to Machine Learning
  • 8.2 Machine Learning Approach
  • 8.3 How Supervised and Unsupervised Learning Models Work
  • 8.4 Scikit-Learn
  • 8.5 Supervised Learning Models - Linear Regression, Logistic Regression
  • 8.6 K Nearest Neighbors (K-NN) Model
  • 8.7 Unsupervised Learning Models: Clustering, Dimensionality Reduction
  • 8.8 Pipeline  Model Persistence  Model Evaluation - Metric Functions

Lesson 9 - Natural Language Processing with Scikit-Learn

  • 9.1 NLP Overview
  • 9.2 NLP Approach for Text Data
  • 9.3 NLP Environment Setup
  • 9.4 NLP Sentence analysis  NLP Applications
  • 9.5 Major NLP Libraries  Scikit-Learn Approach
  • 9.6 Scikit - Learn Approach Built - in Modules  Scikit - Learn Approach Feature Extraction
  • 9.7 Bag of Words  Extraction Considerations
  • 9.8 Scikit - Learn Approach Model Training
  • 9.9 Scikit - Learn Grid Search and Multiple Parameters
  • 9.10 Pipeline

Lesson 10 - Data Visualization in Python using Matplotlib

  • 10.1 Introduction to Data Visualization
  • 10.2 Python Libraries  Plots
  • 10.3 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
  • 10.4 Types of Plots and Seaborn

Lesson 11 - Data Science with Python Web Scraping

  • 11.1 Web Scraping
  • 11.2 Common Data/Page Formats on The Web
  • 11.3 The Parser  Importance of Objects
  • 11.4 Understanding the Tree  Searching the Tree
  • 11.5 Navigating options  Modifying the Tree
  • 11.6 Parsing Only Part of the Document
  • 11.7 Printing and Formatting  Encoding

Lesson 12 - Python integration with Hadoop, MapReduce and Spark

  • 12.1 Need for Integrating Python with Hadoop
  • 12.2 Big Data Hadoop Architecture
  • 12.3 MapReduce  Cloudera QuickStart VM Set Up
  • 12.4 Apache Spark
  • 12.5 Resilient Distributed Systems (RDD)
  • 12.6 PySpark  Spark Tools
  • 12.7 PySpark Integration with Jupyter Notebook

Exam Certification

Python Online Training Course is intended for clearing the IgmGuru Python Certification exam. The whole training course content is intended by business professionals to induce the most effective jobs within the high MNCs. As a part of this coaching, you may be engaged on real-time projects and assignments that have large implications within the real world business situation so serving to you way your career effortlessly.

At the tip of this educational program, there'll be quizzes that completely replicate the kind of queries asked within the various certification examinations and helps you score higher marks within the certification exam.

IgmGuru Course Completion Certification is awarded on the completion of Project work (on skilled review).

Frequently Asked Questions

A :Yes, Exam fee is included in the training fees.

A :IgmGuru provides the course completion certificate, after clear the exam.

A :IgmGuru offers 24/7 support through email, chat, and calls. we tend to even have a passionate team that gives on-demand help through our community forum. What’s a lot of, you may have lifespan access to the community forum, even once completion of your course with us.

A :Our teaching assistants are a fanatical team of material consultants here to assist you to get certified in your initial try. They have interaction with students proactively to confirm the course path is being followed and assist you to enrich your learning expertise, from a category onboarding to project mentoring and job help. Teaching help is accessible throughout business hours.

A :All of our extremely qualified trainers are AWS certified with years of expert coaching and dealing within the cloud domain. every one of them has had a rigorous choice method that has profile screening, technical analysis, and a coaching demo before they're certified to coach for us. we have a tendency to conjointly make sure that solely those trainers with a high alumni rating stay on our faculty.

A :The tools you’ll need to attend training are • Windows: Windows XP SP3 or higher • Mac: OSX 10.6 or higher • Internet speed: Preferably 512 Kbps or higher Headset, speakers, and microphone: You’ll need headphones or speakers to hear instruction clearly, as well as a microphone to talk to others. You can use a headset with a built-in microphone, or separate speakers and a microphone.

A :All of the categories square measure conducted via live on-line streaming. they're interactive sessions that modify you to raise queries and participate in discussions throughout category time. We do, however, offer recordings of every session you attend for your future reference. categories square measure attended by a world audience to complement your learning expertise.

Data Science With Python

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