Data Science with Python Course Overview

The Python Data Science Course teaches you to master the concepts of Python programming. Through this Python Data Science training, you will gain knowledge in data analysis, Machine Learning, data visualization, web scraping, and Natural Language Processing. Upon course completion, you will master the essential tools of Data Science with Python.

Data Science with Python Key Features

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At Simplilearn, we value the trust of our patrons immensely. But, if you feel that a course does not meet your expectations, we offer a 7-day money-back guarantee. Just send us a refund request via email within 7 days of purchase and we will refund 100% of your payment, no questions asked!
  • 68 hours of blended learning
  • 4 industry-based projects
  • Interactive learning with Jupyter notebooks labs
  • Lifetime access to self-paced learning
  • Dedicated mentoring session from faculty of industry experts

Skills Covered

  • Data wrangling
  • Data exploration
  • Data visualization
  • Mathematical computing
  • Web scraping
  • Hypothesis building
  • Python programming concepts
  • NumPy and SciPy package
  • Scikit-Learn package for Natural Language Processing

Training Options

Self-Paced Learning

₹ 21,999

  • Lifetime access to high-quality self-paced e-learning content curated by industry experts
  • 24x7 learner assistance and support
Enroll now

Blended Learning

₹ 22,999

  • 90 days of flexible access to online classes
  • Lifetime access to high-quality self-paced e-learning content and live class recordings
  • 24x7 learner assistance and support
  • Classes starting in Bangalore from:-
29th Feb: Weekend Class
7th Mar: Weekend Class
Enroll now

Corporate Training

Customized to your team's needs

  • Blended learning delivery model (self-paced eLearning and/or instructor-led options)
  • Flexible pricing options
  • Enterprise grade Learning Management System (LMS)
  • Enterprise dashboards for individuals and teams
  • 24x7 learner assistance and support

Data Science with Python Course Curriculum

Eligibility

The demand for Data Science professionals has surged, making this course well-suited for participants at all levels of experience. This Data Science with Python training is beneficial for analytics professionals willing to work with Python, software and IT professionals interested in the field of analytics, and anyone with a genuine interest in Data Science.
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Pre-requisites

To best understand the Data Science with Python course, it is recommended that you begin with the courses including, Python Basics, Math Refresher, Data Science in Real Life, and Statistics Essentials for Data Science. These courses are offered as free companions with this program.
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Course Content

  • Data Science with Python

    Preview

    • Lesson 00 - Course Overview

      04:34Preview
      • 0.001 Course Overview
        04:34

    • Lesson 01 - Data Science Overview

      20:27Preview
      • 1.001 Introduction to Data Science
        08:42
      • 1.002 Different Sectors Using Data Science
        05:59
      • 1.003 Purpose and Components of Python
        05:02
      • 1.4 Quiz
      • 1.005 Key Takeaways
        00:44

    • Lesson 02 - Data Analytics Overview

      18:20Preview
      • 2.001 Data Analytics Process
        07:21
      • 2.2 Knowledge Check
      • 2.3 Exploratory Data Analysis(EDA)
      • 2.4 EDA-Quantitative Technique
      • 2.005 EDA - Graphical Technique
        00:57
      • 2.006 Data Analytics Conclusion or Predictions
        04:30
      • 2.007 Data Analytics Communication
        02:06
      • 2.8 Data Types for Plotting
      • 2.009 Data Types and Plotting
        02:29
      • 2.11 Quiz
      • 2.012 Key Takeaways
        00:57
      • 2.10 Knowledge Check

    • Lesson 03 - Statistical Analysis and Business Applications

      23:53Preview
      • 3.001 Introduction to Statistics
        01:31
      • 3.2 Statistical and Non-statistical Analysis
      • 3.003 Major Categories of Statistics
        01:34
      • 3.4 Statistical Analysis Considerations
      • 3.005 Population and Sample
        02:15
      • 3.6 Statistical Analysis Process
      • 3.007 Data Distribution
        01:48
      • 3.8 Dispersion
      • 3.9 Knowledge Check
      • 3.010 Histogram
        03:59
      • 3.11 Knowledge Check
      • 3.012 Testing
        08:18
      • 3.13 Knowledge Check
      • 3.014 Correlation and Inferential Statistics
        02:57
      • 3.15 Quiz
      • 3.016 Key Takeaways
        01:31

    • Lesson 04 - Python Environment Setup and Essentials

      23:58Preview
      • 4.001 Anaconda
        02:54
      • 4.2 Installation of Anaconda Python Distribution (contd.)
      • 4.003 Data Types with Python
        13:28
      • 4.004 Basic Operators and Functions
        06:26
      • 4.5 Quiz
      • 4.006 Key Takeaways
        01:10

    • Lesson 05 - Mathematical Computing with Python (NumPy)

      30:31Preview
      • 5.001 Introduction to Numpy
        05:30
      • 5.2 Activity-Sequence it Right
      • 5.003 Demo 01-Creating and Printing an ndarray
        04:50
      • 5.4 Knowledge Check
      • 5.5 Class and Attributes of ndarray
      • 5.006 Basic Operations
        07:04
      • 5.7 Activity-Slice It
      • 5.8 Copy and Views
      • 5.009 Mathematical Functions of Numpy
        05:01
      • 5.010 Analyse GDP of Countries
      • 5.011 Assignment 01 Demo
        03:55
      • 5.012 Analyse London Olympics Dataset
      • 5.013 Assignment 02 Demo
        03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways
        00:55

    • Lesson 06 - Scientific computing with Python (Scipy)

      23:35Preview
      • 6.001 Introduction to SciPy
        06:57
      • 6.002 SciPy Sub Package - Integration and Optimization
        05:51
      • 6.3 Knowledge Check
      • 6.4 SciPy sub package
      • 6.005 Demo - Calculate Eigenvalues and Eigenvector
        01:36
      • 6.6 Knowledge Check
      • 6.007 SciPy Sub Package - Statistics, Weave and IO
        05:46
      • 6.008 Solving Linear Algebra problem using SciPy
      • 6.009 Assignment 01 Demo
        01:20
      • 6.010 Perform CDF and PDF using Scipy
      • 6.011 Assignment 02 Demo
        00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways
        01:10

    • Lesson 07 - Data Manipulation with Pandas

      47:34Preview
      • 7.001 Introduction to Pandas
        12:29
      • 7.2 Knowledge Check
      • 7.003 Understanding DataFrame
        05:31
      • 7.004 View and Select Data Demo
        05:34
      • 7.005 Missing Values
        03:16
      • 7.006 Data Operations
        09:56
      • 7.7 Knowledge Check
      • 7.008 File Read and Write Support
        00:31
      • 7.9 Knowledge Check-Sequence it Right
      • 7.010 Pandas Sql Operation
        02:00
      • 7.011 Analyse the Federal Aviation Authority Dataset using Pandas
      • 7.012 Assignment 01 Demo
        04:09
      • 7.013 Analyse NewYork city fire department Dataset
      • 7.014 Assignment 02 Demo
        02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways
        01:34

    • Lesson 08 - Machine Learning with Scikit–Learn

      01:02:10Preview
      • 8.001 Machine Learning Approach
        03:57
      • 8.002 Steps One and Two
        01:00
      • 8.3 Steps Three and Four
      • 8.004 How it Works
        01:24
      • 8.005 Steps Five and Six
        01:54
      • 8.006 Supervised Learning Model Considerations
        00:30
      • 8.008 ScikitLearn
        02:10
      • 8.010 Supervised Learning Models - Linear Regression
        11:19
      • 8.011 Supervised Learning Models - Logistic Regression
        08:43
      • 8.012 Unsupervised Learning Models
        10:40
      • 8.013 Pipeline
        02:37
      • 8.014 Model Persistence and Evaluation
        05:45
      • 8.15 Knowledge Check
      • 8.016 Analysing Ad Budgets for different media channels
      • 8.017 Assignment One
        05:45
      • 8.018 Building a model to predict Diabetes
      • 8.019 Assignment Two
        05:14
      • Knowledge Check
      • 8.021 Key Takeaways
        01:12

    • Lesson 09 - Natural Language Processing with Scikit Learn

      49:03
      • 9.001 NLP Overview
        10:42
      • 9.2 NLP Applications
      • 9.3 Knowledge Check
      • 9.004 NLP Libraries-Scikit
        12:29
      • 9.5 Extraction Considerations
      • 9.006 Scikit Learn-Model Training and Grid Search
        10:17
      • 9.007 Analysing Spam Collection Data
      • 9.008 Demo Assignment 01
        06:32
      • 9.009 Sentiment Analysis using NLP
      • 9.010 Demo Assignment 02
        08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway
        01:03

    • Lesson 10 - Data Visualization in Python using matplotlib

      32:43Preview
      • 10.001 Introduction to Data Visualization
        08:01
      • 10.2 Knowledge Check
      • 10.3 Line Properties
      • 10.004 (x,y) Plot and Subplots
        10:01
      • 10.5 Knowledge Check
      • 10.006 Types of Plots
        09:32
      • 10.007 Draw a pair plot using seaborn library
      • 10.008 Assignment 01 Demo
        02:23
      • 10.009 Analysing Cause of Death
      • 10.010 Assignment 02 Demo
        01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways
        00:59

    • Lesson 11 - Web Scraping with BeautifulSoup

      52:26Preview
      • 11.001 Web Scraping and Parsing
        12:50
      • 11.2 Knowledge Check
      • 11.003 Understanding and Searching the Tree
        12:56
      • 11.4 Navigating options
      • 11.005 Demo3 Navigating a Tree
        04:22
      • 11.6 Knowledge Check
      • 11.007 Modifying the Tree
        05:37
      • 11.008 Parsing and Printing the Document
        09:05
      • 11.009 Web Scraping of Simplilearn Website
      • 11.010 Assignment 01 Demo
        01:55
      • 11.011 Web Scraping of Simplilearn Website Resource page
      • 11.012 Assignment 02 demo
        04:57
      • 11.13 Quiz
      • 11.014 Key takeaways
        00:44

    • Lesson 12 - Python integration with Hadoop MapReduce and Spark

      40:39Preview
      • 12.001 Why Big Data Solutions are Provided for Python
        04:55
      • 12.2 Hadoop Core Components
      • 12.003 Python Integration with HDFS using Hadoop Streaming
        07:20
      • 12.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
        08:52
      • 12.5 Knowledge Check
      • 12.006 Python Integration with Spark using PySpark
        07:43
      • 12.007 Demo 02 - Using PySpark to Determine Word Count
        04:12
      • 12.8 Knowledge Check
      • 12.009 Determine the wordcount
      • 12.010 Assignment 01 Demo
        02:47
      • 12.011 Display all the airports based in New York using PySpark
      • 12.012 Assignment 02 Demo
        03:30
      • 12.13 Quiz
      • 12.014 Key takeaways
        01:20

    • Practice Projects

      • IBM HR Analytics Employee Attrition Modeling.
  • Free Course

  • Math Refresher

    Preview

    • Math Refresher

      30:36Preview
      • Math Refresher
        30:36
  • Free Course

  • Data Science in Real life

    Preview

    • Lesson 1 - Course Objective

      • Learning Objectives

    • Lesson 2 - Defining Data Science

      12:46Preview
      • Learning Objectives
      • 1.1 What is data science
        02:37
      • 1.2 There are many paths to data science
        03:55
      • 1.3 Any advice for new data scientist
        02:59
      • 1.4 What is the cloud
        03:15

    • Lesson 3 - What do Data Science People do

      11:24
      • Learning Objectives
      • 2.1 A day in the life of a data science person
        03:53
      • 2.2 R versus Python
        01:51
      • 2.3 Data science tools and technology
        05:40

    • Lesson 4 - Data Science in Business

      10:40Preview
      • Learning Objectives
      • 3.1 How should companies get started in data science
        03:00
      • 3.2 Recruiting for data science
        07:40

    • Lesson 5 - Use Cases for Data Science

      06:28
      • Learning Objectives
      • 4.1 Applications of data science
        06:28

    • Lesson 6 - Data Science People

      01:05
      • Learning Objectives
      • 5.1 Things data science people say
        01:05
      • Unlocking IBM Certificate
  • Free Course

  • Python for Data Science

    Preview

    • Lesson 1 - Welcome

      02:28Preview
      • Welcome
        02:28
      • Learning Objectives

    • Lesson 2 - Python Basics

      11:55Preview
      • 2.1 Learning Objectives
      • 2.2 Your first program
        01:15
      • 2.3 Types
        02:57
      • 2.4 Expressions and Variables
        03:50
      • 2.5 Write your First Python Code
      • 2.6 String Operations
        03:53
      • 2.7 String Operations

    • Lesson 3 - Python Data Structures

      16:22Preview
      • 3.1 Learning Objectives
      • 3.2 Lists and Tuples
        08:46
      • 3.3 Lists and Tuples
      • 3.4 Sets
        05:12
      • 3.5 Sets
      • 3.6 Dictionaries
        02:24
      • 3.7 Dictionaries

    • Lesson 4 - Python Programming Fundamentals

      41:08Preview
      • 4.1 Learning Objectives
      • 4.2 Conditions and Branching
        10:13
      • 4.3 Conditions and Branching
      • 4.4 Loops
        06:40
      • 4.5 Loops
      • 4.6 Functions
        13:28
      • 4.7 Functions
      • 4.8 Objects and Classes
        10:47
      • 4.9 Objects and Classes

    • Lesson 5 - Working with Data in Python

      12:35
      • 5.1 Learning Objectives
      • 5.2 Reading files with open
        03:38
      • 5.3 Reading Files
      • 5.4 Writing files with open
        02:49
      • 5.5 Writing Files
      • 5.6 Loading data with Pandas
        04:07
      • 5.7 Working with and Saving data with Pandas
        02:01
      • 5.8 Loading Data and Viewing Data

    • Lesson 6 - Working with Numpy Arrays

      18:26
      • 6.1 Learning Objectives
      • 6.2 Numpy One-Dimensional Arrays
        11:18
      • 6.3 Working with One-Dimensional Numpy Arrays
      • 6.4 Numpy Two-Dimensional Arrays
        07:08
      • 6.5 Working with Two-Dimensional Numpy Arrays

    • Lesson 7 - Course Summary

      01:13
      • Course Summary
        01:13
      • Unlocking IBM Certificate
  • Free Course

  • Statistics Essential for Data Science

    Preview

    • Lesson 1 Introduction

      02:55Preview
      • 1.1 Introduction
        02:55

    • Lesson 2 Sample or population data

      03:56Preview
      • 2.1 Sample or population data
        03:56

    • Lesson 3 The fundamentals of descriptive statistics

      21:18Preview
      • 3.1 The fundamentals of descriptive statistics
        03:18
      • 3.2 Levels of measurement
        02:57
      • 3.3 Categorical variables. Visualization techniques for categorical variables
        04:06
      • 3.4 Numerical variables. Using a frequency distribution table
        03:24
      • 3.5 Histogram charts
        02:27
      • 3.6 Cross tables and scatter plots
        05:06

    • Lesson 4 Measures of central tendency, asymmetry, and variability

      25:17Preview
      • 4.1 Measures of central tendency, asymmetry, and variability
        04:24
      • 4.2 Measuring skewness
        02:43
      • 4.3 Measuring how data is spread out calculating variance
        05:58
      • 4.4 Standard deviation and coefficient of variation
        04:54
      • 4.5 Calculating and understanding covariance
        03:31
      • 4.6 The correlation coefficient
        03:47

    • Lesson 5 Practical example descriptive statistics

      14:30
      • 5.1 Practical example descriptive statistics
        14:30

    • Lesson 6 Distributions

      16:17Preview
      • 6.1 Distributions
        01:02
      • 6.2 What is a distribution
        03:40
      • 6.3 The Normal distribution
        03:45
      • 6.4 The standard normal distribution
        02:51
      • 6.5 Understanding the central limit theorem
        03:40
      • 6.6 Standard error
        01:19

    • Lesson 7 Estimators and Estimates

      23:36Preview
      • 7.1 Estimators and Estimates
        02:36
      • 7.2 Confidence intervals - an invaluable tool for decision making
        06:31
      • 7.3 Calculating confidence intervals within a population with a known variance
        02:30
      • 7.4 Student’s T distribution
        03:14
      • 7.5 Calculating confidence intervals within a population with an unknown variance
        04:07
      • 7.6 What is a margin of error and why is it important in Statistics
        04:38

    • Lesson 8 Confidence intervals advanced topics

      14:27Preview
      • 8.1 Confidence intervals advanced topics
        04:47
      • 8.2 Calculating confidence intervals for two means with independent samples (part One)
        04:36
      • 8.3 Calculating confidence intervals for two means with independent samples (part two)
        03:40
      • 8.4 Calculating confidence intervals for two means with independent samples (part three)
        01:24

    • Lesson 9 Practical example inferential statistics

      09:37
      • 9.1 Practical example inferential statistics
        09:37

    • Lesson 10 Hypothesis testing Introduction

      12:36
      • 10.1 Hypothesis testing Introduction
        04:56
      • 10.2 Establishing a rejection region and a significance level
        04:20
      • 10.3 Type I error vs Type II error
        03:20

    • Lesson 11 Hypothesis testing Let's start testing!

      26:39Preview
      • 11.1 Hypothesis testing Let's start testing!
        06:07
      • 11.2 What is the p-value and why is it one of the most useful tool for statisticians
        03:55
      • 11.3 Test for the mean. Population variance unknown
        04:26
      • 11.4 Test for the mean. Dependent samples
        04:45
      • 11.5 Test for the mean. Independent samples (Part One)
        03:38
      • 11.6 Test for the mean. Independent samples (Part Two)
        03:48

    • Lesson 12 Practical example hypothesis testing

      06:31
      • 12.1 Practical example hypothesis testing
        06:31

    • Lesson 13 The fundamentals of regression analysis

      18:32Preview
      • 13.1 The fundamentals of regression analysis
        01:02
      • 13.2 Correlation and causation
        04:06
      • 13.3 The linear regression model made easy
        05:02
      • 13.4 What is the difference between correlation and regression
        01:28
      • 13.5 A geometrical representation of the linear regression model
        01:18
      • 13.6 A practical example - Reinforced learning
        05:36

    • Lesson 14 Subtleties of regression analysis

      23:25Preview
      • 14.1 Subtleties of regression analysis
        02:04
      • 14.2 What is Rsquared and how does it help us
        05:00
      • 14.3 The ordinary least squares setting and its practical applications
        02:08
      • 14.4 Studying regression tables
        04:34
      • 14.5 The multiple linear regression model
        02:42
      • 14.6 Adjusted R-squared
        04:57
      • 14.7 What does the F-statistic show us and why we need to understand it
        02:00

    • Lesson 15 Assumptions for linear regression analysis

      19:16
      • 15.1 Assumptions for linear regression analysis
        02:11
      • 15.2 Linearity
        01:40
      • 15.3 No endogeneity
        03:43
      • 15.4 Normality and homoscedasticity
        05:09
      • 15.5 No autocorrelation
        03:11
      • 15.6 No multicollinearity
        03:22

    • Lesson 16 Dealing with categorical data

      05:20
      • 16.1 Dealing with categorical data
        05:20

    • Lesson 17 Practical example regression analysis

      14:42
      • 17.1 Practical example regression analysis
        14:42

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Industry Project

  • Project 1

    Products rating prediction for Amazon

    Help Amazon, a US-based e-commerce company, improve its recommendation engine by predicting ratings for the non-rated products and adding them to recommendations accordingly.

    Products rating prediction for Amazon
  • Project 2

    Demand Forecasting for Walmart

    Predict accurate sales for 45 Walmart stores, considering the impact of promotional markdown events. Check if macroeconomic factors have an impact on sales.

    Demand Forecasting for Walmart
  • Project 3

    Improving customer experience for Comcast

    Provide Comcast, a US-based global telecom company, key recommendations to improve customer experience by identifying and improving problem areas that lower customer satisfaction.

    Improving customer experience for Comcast
  • Project 4

    Attrition Analysis for IBM

    IBM, a leading US-based IT company, wants to identify the factors that influence employee attrition by building a logistics regression model that can help predict employee churn.

    Attrition Analysis for IBM
  • Project 5

    NYC 311 Service Request Analysis

    Perform a service request data analysis of New York City 3-1-1 calls. Focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.

    NYC 311 Service Request Analysis
  • Project 6

    MovieLens Dataset Analysis

    A research team is working on information filtering, collaborative filtering, and recommender systems. Perform analysis using Exploratory Data Analysis technique for user datasets.

    MovieLens Dataset Analysis
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Data Science with Python Exam & Certification

Python for Data Science Certification Training Course in Bangalore, India

  • How do I earn my Simplilearn certificate?

     

    The candidates need to meet the following conditions to achieve the Data Scientist with Python Certification:

    • Complete 85% of the course
    • Attend one complete batch.
    • Submit one project and get it assessed by the lead trainer after updating the deliverables of the project in the LMS.
    • Clear one of the two simulation tests with 60% passing score.

    Note: it is necessary for the candidates to complete one project and clear the online exam with at least 60% passing score. After this, they will be awarded a three-month experience certificate for implementing the projects using Python and they will become a certified data scientist.

  • Do you provide any practice tests as part of this course?

    Yes, we provide 1 practice test as part of our course to help you prepare for the actual certification exam. You can try this Free Data Science with Python Practice Test to understand the type of tests that are part of the course curriculum.  

Data Science with Python Course Reviews

  • Akash Raj

    Akash Raj

    Technology Engineer, Bangalore

    The instructor not only delivers the lecture but also focuses on practical aspects related to the subject. This is something about the course that really impressed me.

  • Kiran Kumar

    Kiran Kumar

    Bangalore

    I recently enrolled in the Data Scientist Master’s Program at Simplilearn. The syllabus is systematically structured, and the Live sessions are explained with real-time examples. This makes the course more accessible to freshers with basic knowledge. Looking forward to completing it. Thanks, Simplilearn Team.

  • Shweta Chauhan

    Shweta Chauhan

    Bangalore

    Thanks a lot, Sunny, for the immense support and guidance throughout the project, and for your patience while calmly helping me fix both small and big problems. You have excellent and in-depth knowledge about Python and the alternative options you taught me. I'm delighted to share my opinion about my experience.

  • Rajesh Dubey

    Rajesh Dubey

    Bangalore

    Simplilearn is a great learning platform for aspirants. It provides online learning with global intellectual faculties. It offers unparallel service and is the best place to attain professional knowledge and grow in one's career. I am very, very thankful to Simplilearn.

  • V S Baanu Prasanna Ramesh

    V S Baanu Prasanna Ramesh

    Bangalore

    Simplilearn helped me in learning Data Science with Python. It gave me the required confidence to restart my career after a break. The best part was its flexibility and the freedom to learn at your own place and pace. Thank you so much!

  • Aashish Kumar

    Aashish Kumar

    Patna

    I completed the Data Science with Python course at Simplilearn. The faculty, Prashanth Nair, was extremely knowledgeable, and the entire class appreciated his way of teaching. Simplilearn's support team was very accommodating and quick in providing responses. I was able to grab a 30% hike in my salary after getting certified.

  • Nikhil Lohakare

    Nikhil Lohakare

    Pune

    The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant.

  • Mukesh Pandey

    Mukesh Pandey

    Hyderabad

    Simplilearn is an excellent platform for online learning. Their course curriculum is comprehensive and up to date. We get lifetime access to the recorded sessions in case we need to refresh our understanding. If you are looking to upskill, I suggest you sign up with Simplilearn. They offer classes in almost all disciplines.

  • Dastagiri Durgam

    Dastagiri Durgam

    Hyderabad

    Incredible mentorship, and amazing and unique lectures. Simplilearn provides a great way to learn with self-paced videos and recordings of online sessions. Thanks, Simplilearn, for providing quality education.

  • C Muthu Raman

    C Muthu Raman

    Pune

    Simplilearn facilitates a brilliant platform to acquire new & relevant skills at ease. Well laid out course content and expert faculty ensure an excellent learning experience.

  • Surendaran Baskaran

    Surendaran Baskaran

    Coimbatore

    I took the Data Science with Python course with Simplilearn. The instructor is knowledgeable and shares their skills and knowledge. My learning experience has been outstanding with Simplilearn. The practice labs and materials are helpful for better learning. Thank you, Simplilearn. Happy Learning!!

  • Shiv Sharma

    Shiv Sharma

    Mumbai

    Prashant Nair is an awesome faculty. The way he simplifies, relates and explains topics is outstanding. I would love to enroll for and attend all his classes.

  • Jatin Alwani

    Jatin Alwani

    Student at Lovely Professional University, Jalandhar

    I have enrolled for Data Science certification from Simplilearn. The course materials are great and the trainers are also very helpful. The industry-based project is the best part of the course. Simplilearn is better than any others in the market.

  • Shoeb Mohammad

    Shoeb Mohammad

    Analyst at Accenture, Delhi

    I had joined the Data Science certification from Simplilearn. The course content was really good. The trainer puts a lot of efforts into explaining every detail which made the learning very absorbing. The customer support is always available whenever you need help. I actually feel one step forward towards my goal. Thank you.

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Why Simplilearn

Simplilearn’s Blended Learning model brings classroom learningexperience online with its world-class LMS. It combines instructor-led training, self-paced learning and personalized mentoring to provide an immersive learning experience

  • Self-Paced Online Video

    A 360-degree learning approach that you can adapt to your learning style

  • Live Virtual Classroom

    Engage and learn more with these live and highly-interactive classes alongside your peers

  • 24/7 Teaching Assistance

    Keep engaged with integrated teaching assistance in your desktop and mobile learning

  • Online Practice Labs

    Projects provide you with sample work to show prospective employers

  • Applied Projects

    Real-world projects relevant to what you’re learning throughout the program

  • Learner Social Forums

    A support team focused on helping you succeed alongside a peer community

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Data Science with Python Training FAQs

  • Why you should take this Python for Data Science Course?

  • What are the course objectives?

     

    The achievement of Python lies in the fact that the trainers in the US now introduce computer science and programming using the language Python and not the long-renowned Java language. It is also estimated that Python has become a necessary skill to be mastered for 46% of data science jobs.

    The Data Science with Python course in Bangalore gives a complete overview of Python language and explores the various packages and libraries of Python that are necessary to implement natural language processing, web scraping, data analysis, machine learning, and data analysis.

  • What skills will you learn in this Python for Data Science Training Course?

    At the end of this Python for Data Science training course in Bangalore, candidates will be able to:

    • Extract useful data from websites by performing web scrapping using Python
    • Use the Scikit-Learn package for natural language processing
    • Gain expertise in machine learning using the Scikit-Learn package
    • Perform scientific and technical computing using the SciPy package and its sub-packages such as Integrate, Optimize, Statistics, IO, and Weave
    • Understand the essential concepts of Python programming such as data types, tuples, lists, dicts, basic operators and functions
    • Gain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building, and testing. You will also learn the basics of statistics.
    • Integrate Python with Hadoop, Spark and MapReduce
    • Use the matplotlib library of Python for data visualization
    • Gain an in-depth understanding of supervised learning and unsupervised learning models such as linear regression, logistic regression, clustering, dimensionality reduction, K-NN and pipeline
    • Perform data analysis and manipulation using data structures and tools provided in the Pandas package
    • Perform high-level mathematical computing using the NumPy package and its large library of mathematical functions
    • Install the required Python environment and other auxiliary tools and libraries

  • Who should take this Python for Data Science course?

    The Data Science with Python course in Bangalore comes with no prerequisite. Concerning the assistance for coding, there is an additional course on Python basics included in the main course.

    The world of data Science has the capability of generating potential job opportunities for data scientists, specifically for professionals like:

    • Experienced professionals who would like to harness data science in their fields
    • IT professionals interested in pursuing a career in analytics
    • Analytics professionals who want to work with Python
    • Anyone with a genuine interest in the field of data science
    • Graduates looking to build a career in analytics and data science
    • Software professionals looking to get into the field of analytics
    •  

  • What projects included with this Python for Data Science certification course?

     

    The Python for Data Science certification course in Bangalore incorporates 4 industry-oriented, real-life projects. The candidates need to complete one out of the four projects as part of the course completion process. The project will then be accessed by our subject matter experts. The projects are listed below:

    Project 1: NYC 311 Service Request Analysis

    Telecommunication: Perform a service request data analysis of New York City 311 calls. You will focus on data wrangling techniques to understand patterns in the data and visualize the major complaint types.

    Project 2: MovieLens Dataset Analysis

    Engineering: The GroupLens Research Project is a research group in the Department of Computer Science and Engineering at the University of Minnesota. The researchers of this group are involved in several research projects in the fields of information filtering, collaborative filtering, and recommender systems. Here, we ask you to perform an analysis using the Exploratory Data Analysis technique for user datasets.

    Project 3: Stock Market Data Analysis

    Stock Market: As a part of this project, you will import data using Yahoo data reader from the following companies: Yahoo, Apple, Amazon, Microsoft, and Google. You will perform fundamental analytics, including plotting, closing price, plotting stock trade by volume, performing daily return analysis, and using pair plot to show the correlation between all of the stocks.

    Project 4: Titanic Dataset Analysis

    Hazard: On April 15, 1912, the Titanic sank after colliding with an iceberg, killing 1502 out of 2224 passengers and crew. This tragedy shocked the world and led to better safety regulations for ships. Here, we ask you to perform an analysis using the exploratory data analysis technique, in particular applying machine learning tools to predict which passengers survived the tragedy.

  • How much does a Data Scientist get paid in Bangalore?

     

    It is estimated by PayScale that an average salary of Rs 606,409 is drawn by a Data Scientist in Bangalore. The number further improves for professionals undertaking a Data Science with Python program.

  • What are different roles available for data science Industry in bangalore

     

    Bangalore provides the opportunity of roles like Statistical Analyst and Data Scientist IT/Retail within the Business Intelligence & Data Science domain.

  • What are the top companies hire Data Scientists in Bangalore?

     

    Companies in Bangalore which are in continuous search for professionals proficient in Data Science & Business Intelligence are JP Morgan, Honest Bee, Accenture, Quaero, and Happiest Minds.

  • What are the system requirements?

     

    The system needs to have the following specifications to run Python:

    • OS: 32 or 62 bit
    • Memory: 1GB RAM

    Anaconda and Jupyter notebooks are used for the instructions. Their installation process is dealt with in the e-learning videos.

  • Who are our instructors and how are they selected?

    Simplilearn appoints faculty who possess high alumni rating. The selection process incorporates stages like profile screening, technical assessment, and training demo. Trainers even after completing the selection process should have a proven teaching experience of 10+ years.

  • What are the modes of training offered for this Python for Data Science course?

     

    There are two training modes provided by Simplilearn for Python for Data Science course in Bangalore. They are:

    Online Self-Learning: Pre-recorded videos are provided to the candidates and they can complete the course at their convenient learning pace.

    Live Virtual Classroom or Online Classroom: Instructor-led online classes are provided that basically involves video conferencing. Candidates can attend the class via their desktop and enhance their learning experience.

    All of the hands-on exercises are carried out in the open source environment called as WinPython portable distribution. During the training, its installation procedure will be explained in detail.

  • What if I miss a class?

    On missing a class, the candidate can refer to its recording that is maintained for each class to benefit them in future.

  • Can I cancel my enrollment? Will I get a refund?

     

    Yes, the candidate can cancel his enrollment if necessary. The amount after deduction of the administration fee will be refunded to the candidate. Read our Refund Policy to know more.

  • Who provides the certification?

     

    To achieve the data scientist with Python certificate, candidates are required to complete the course, get the completed project assessed, and pass the online exam with at least 80% passing score.

  • Are there any group discounts for classroom training programs?

     

    Yes, there are group discount packages available for the classroom training programs. Our support team can be contacted via the Help & Support to get the details.

  • How do I enroll for the Data Science with Python online training?

     

    Enrollment for the Data Science with Python online training in Bangalore requires completing an online payment via:

    • Paypal
    • American Express
    • Visa Credit or Debit Card
    • Diner’s Club
    • MasterCard

    The payment receipt along with further information will be emailed to the candidates thereafter.

  • What is Global Teaching Assistance?

     

    From class onboarding to project guidance and job assistance, the mentors of Simplilearn support the candidates throughout their course path. The mentors enrich the learning experience of the candidates and engage them so that they have a clear understanding of every topic covered in the course.

    Teaching assistance is provided during business hours.

  • What is covered under the 24/7 Support promise?

    We offer 24/7 support through email, chat, and calls. We also have a dedicated team that provides on-demand assistance through our community forum. What’s more, you will have lifetime access to the community forum, even after completion of your course with us.

  • * Disclaimer

    * The projects have been built leveraging real publicly available data-sets of the mentioned organizations.

  • What is Python?

    Python is an object-oriented programming language with integrated dynamic semantics, used primarily for application and web development. The widely used language offers dynamic binding and dynamic typing options.
     

  • Can I learn data science with Python online?

    The rapid evolution of learning methodologies, thanks to the influx of technology, has increased the ease and efficiency of online learning, making it possible to learn at your own pace. Simplilearn's Data Science with Python Certification training provides live classes and access to study materials from anywhere and at any time. Our extensive (and growing) collection of blogs, tutorials, and YouTube videos will help you get up to speed on the main concepts. Even after your class ends, we provide a 24/7 support system to help you with any questions or concerns you may have.
     

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