Course description

  • 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.

Course preview

    • Lesson 00 - Course Overview 04:34
      • 0.001 Course Overview 04:34
    • Lesson 01 - Data Science Overview 20:27
      • 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 18:20
      • 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
      • 2.10 Knowledge Check
      • 2.11 Quiz
      • 2.012 Key Takeaways 00:57
    • Lesson 03 - Statistical Analysis and Business Applications 23:53
      • 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 23:58
      • 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) 30:31
      • 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
      • 5.10 Assignment 01
      • 5.011 Assignment 01 Demo 03:55
      • 5.12 Assignment 02
      • 5.013 Assignment 02 Demo 03:16
      • 5.14 Quiz
      • 5.015 Key Takeaways 00:55
    • Lesson 06 - Scientific computing with Python (Scipy) 23:35
      • 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
      • 6.8 Assignment 01
      • 6.009 Assignment 01 Demo 01:20
      • 6.10 Assignment 02
      • 6.011 Assignment 02 Demo 00:55
      • 6.12 Quiz
      • 6.013 Key Takeaways 01:10
    • Lesson 07 - Data Manipulation with Pandas 47:34
      • 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
      • 7.11 Assignment 01
      • 7.012 Assignment 01 Demo 04:09
      • 7.13 Assignment 02
      • 7.014 Assignment 02 Demo 02:34
      • 7.15 Quiz
      • 7.016 Key Takeaways 01:34
    • Lesson 08 - Machine Learning with Scikit–Learn 1:02:10
      • 8.001 Machine Learning Approach 03:57
      • 8.002 Steps 1 and 2 01:00
      • 8.3 Steps 3 and 4
      • 8.004 How it Works 01:24
      • 8.005 Steps 5 and 6 01:54
      • 8.006 Supervised Learning Model Considerations 00:30
      • 8.7 Knowledge Check
      • 8.008 Scikit-Learn 02:10
      • 8.9 Knowledge Check
      • 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
      • 8.16 Assignment 01
      • 8.017 Assignment 01 05:45
      • 8.18 Assignment 02
      • 8.019 Assignment 02 05:14
      • 8.20 Quiz
      • 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
      • 9.7 Assignment 01
      • 9.008 Demo Assignment 01 06:32
      • 9.9 Assignment 02
      • 9.010 Demo Assignment 02 08:00
      • 9.11 Quiz
      • 9.012 Key Takeaway 01:03
    • Lesson 10 - Data Visualization in Python using matplotlib 32:46
      • 10.001 Introduction to Data Visualization 08:02
      • 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 09:34
      • 10.7 Assignment 01
      • 10.008 Assignment 01 Demo 02:23
      • 10.9 Assignment 02
      • 10.010 Assignment 02 Demo 01:47
      • 10.11 Quiz
      • 10.012 Key Takeaways 00:59
    • Lesson 11 - Web Scraping with BeautifulSoup 52:27
      • 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 05:38
      • 11.008 Parsing and Printing the Document 09:05
      • 11.9 Assignment 01
      • 11.010 Assignment 01 Demo 01:55
      • 11.11 Assignment 02
      • 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 40:39
      • 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
      • 12.9 Assignment 01
      • 12.010 Assignment 01 Demo 02:47
      • 12.11 Assignment 02
      • 12.012 Assignment 02 Demo 03:30
      • 12.13 Quiz
      • 12.014 Key takeaways 01:20
    • Statistics Essential for Data Science 30:50
      • Statistics for Data Science 30:50
    • Getting Started with Python 20:04
      • Installation 09:31
      • Print and Strings 07:47
      • Math 02:46
    • Variables, Loops and Statements 36:54
      • Variables 04:49
      • While Loops 06:00
      • For Loops 05:00
      • If Statements 06:43
      • If Else Statements 04:01
      • If Elif Else Statements 10:21
    • Functions and Global and Local Variables 28:20
      • Functions 05:03
      • Function Parameters 14:04
      • Global and Local Variables 09:13
    • Understanding Error Detection 11:35
      • Common Python Errors 11:35
    • Working with Files and Classes 15:49
      • Writing to a File 04:29
      • Appending to a File 03:23
      • Reading From a File 03:34
      • Classes 04:23
    • Intermediate Python 39:09
      • Input and Statistics 07:22
      • Import Syntax 06:39
      • Making Modules 06:20
      • Lists vs Tuples and List Manipulation 10:34
      • Dictionaries 08:14
    • Math Refresher 30:36
      • Math Refresher 30:36
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Exam & certification FREE PRACTICE TEST

  • 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.

Course advisor

Alvaro Fuentes
Alvaro Fuentes Founder and Data Scientist at Quant Company

Alvaro is a Data Scientist who founded Quant Company and has also worked as a lead Economic analyst in the Central Bank of Guatemala. He is a M.S. in Quantitative Economics and Applied Mathematics and is actively involved in consulting and training in the data science space.

Reviews

Gaurav Dubey
Gaurav Dubey Associate Consultant at Syntel, Pune

Prior to joining Data Science course with Simplilearn, I had little knowledge about it. The certification helped me to understand the Machine Learning, Web Scraping, Natural Language Processing in detail. The trainer was very helpful and was always there to guide me in every step. The certification helped me to enhance my career from Software Engineer to Associate Consultant with a salary hike. I am planning to take a few more course from Simplilearn in future.

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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.

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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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Solomon Olutu
Solomon Olutu Snr Principal QA Architect at Comcast, Philadelphia

Simplilearn's Data Science with Python training was a great experience. Their trainers are the best that I have come across since I started learning with Silplilearn. He is always prepared for class with a well-documented note session which is also useful for hands-on learning after class to enhance the learning experience. Thanks Simplilearn. This is the best platform that I have come across.

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FAQs

  • 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.

    Our Bangalore Correspondence / Mailing address

    # 53/1 C, Manoj Arcade, 24th Main, 2nd Sector, HSR Layout, Bangalore - 560102, Karnataka, India.

    • Disclaimer
    • PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.