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
- 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
Data Science with Python Course Curriculum
Eligibility
Pre-requisites
Course Content
Data Science with Python
PreviewLesson 00 - Course Overview
04:34Preview0.001 Course Overview
04:34
Lesson 01 - Data Science Overview
20:27Preview1.001 Introduction to Data Science
08:421.002 Different Sectors Using Data Science
05:591.003 Purpose and Components of Python
05:021.4 Quiz
1.005 Key Takeaways
00:44
Lesson 02 - Data Analytics Overview
18:202.001 Data Analytics Process
07:212.2 Knowledge Check
2.3 Exploratory Data Analysis(EDA)
2.4 EDA-Quantitative Technique
2.005 EDA - Graphical Technique
00:572.006 Data Analytics Conclusion or Predictions
04:302.007 Data Analytics Communication
02:062.8 Data Types for Plotting
2.009 Data Types and Plotting
02:292.11 Quiz
2.012 Key Takeaways
00:572.10 Knowledge Check
Lesson 03 - Statistical Analysis and Business Applications
23:53Preview3.001 Introduction to Statistics
01:313.2 Statistical and Non-statistical Analysis
3.003 Major Categories of Statistics
01:343.4 Statistical Analysis Considerations
3.005 Population and Sample
02:153.6 Statistical Analysis Process
3.007 Data Distribution
01:483.8 Dispersion
3.9 Knowledge Check
3.010 Histogram
03:593.11 Knowledge Check
3.012 Testing
08:183.13 Knowledge Check
3.014 Correlation and Inferential Statistics
02:573.15 Quiz
3.016 Key Takeaways
01:31
Lesson 04 - Python Environment Setup and Essentials
23:58Preview4.001 Anaconda
02:544.2 Installation of Anaconda Python Distribution (contd.)
4.003 Data Types with Python
13:284.004 Basic Operators and Functions
06:264.5 Quiz
4.006 Key Takeaways
01:10
Lesson 05 - Mathematical Computing with Python (NumPy)
30:31Preview5.001 Introduction to Numpy
05:305.2 Activity-Sequence it Right
5.003 Demo 01-Creating and Printing an ndarray
04:505.4 Knowledge Check
5.5 Class and Attributes of ndarray
5.006 Basic Operations
07:045.7 Activity-Slice It
5.8 Copy and Views
5.009 Mathematical Functions of Numpy
05:015.010 Analyse GDP of Countries
5.011 Assignment 01 Demo
03:555.012 Analyse London Olympics Dataset
5.013 Assignment 02 Demo
03:165.14 Quiz
5.015 Key Takeaways
00:55
Lesson 06 - Scientific computing with Python (Scipy)
23:35Preview6.001 Introduction to SciPy
06:576.002 SciPy Sub Package - Integration and Optimization
05:516.3 Knowledge Check
6.4 SciPy sub package
6.005 Demo - Calculate Eigenvalues and Eigenvector
01:366.6 Knowledge Check
6.007 SciPy Sub Package - Statistics, Weave and IO
05:466.008 Solving Linear Algebra problem using SciPy
6.009 Assignment 01 Demo
01:206.010 Perform CDF and PDF using Scipy
6.011 Assignment 02 Demo
00:556.12 Quiz
6.013 Key Takeaways
01:10
Lesson 07 - Data Manipulation with Pandas
47:34Preview7.001 Introduction to Pandas
12:297.2 Knowledge Check
7.003 Understanding DataFrame
05:317.004 View and Select Data Demo
05:347.005 Missing Values
03:167.006 Data Operations
09:567.7 Knowledge Check
7.008 File Read and Write Support
00:317.9 Knowledge Check-Sequence it Right
7.010 Pandas Sql Operation
02:007.011 Analyse the Federal Aviation Authority Dataset using Pandas
7.012 Assignment 01 Demo
04:097.013 Analyse NewYork city fire department Dataset
7.014 Assignment 02 Demo
02:347.15 Quiz
7.016 Key Takeaways
01:34
Lesson 08 - Machine Learning with Scikit–Learn
01:02:10Preview8.001 Machine Learning Approach
03:578.002 Steps 1 and 2
01:008.3 Steps 3 and 4
8.004 How it Works
01:248.005 Steps 5 and 6
01:548.006 Supervised Learning Model Considerations
00:308.7 Knowledge Check
8.008 Scikit-Learn
02:108.9 Knowledge Check
8.010 Supervised Learning Models - Linear Regression
11:198.011 Supervised Learning Models - Logistic Regression
08:438.012 Unsupervised Learning Models
10:408.013 Pipeline
02:378.014 Model Persistence and Evaluation
05:458.15 Knowledge Check
8.016 Analysing Ad Budgets for different media channels
8.017 Assignment 01
05:458.018 Building a model to predict Diabetes
8.019 Assignment 02
05:148.20 Quiz
8.021 Key Takeaways
01:12
Lesson 09 - Natural Language Processing with Scikit Learn
49:03Preview9.001 NLP Overview
10:429.2 NLP Applications
9.3 Knowledge Check
9.004 NLP Libraries-Scikit
12:299.5 Extraction Considerations
9.006 Scikit Learn-Model Training and Grid Search
10:179.007 Analysing Spam Collection Data
9.008 Demo Assignment 01
06:329.009 Sentiment Analysis using NLP
9.010 Demo Assignment 02
08:009.11 Quiz
9.012 Key Takeaway
01:03
Lesson 10 - Data Visualization in Python using matplotlib
32:4310.001 Introduction to Data Visualization
08:0110.2 Knowledge Check
10.3 Line Properties
10.004 (x,y) Plot and Subplots
10:0110.5 Knowledge Check
10.006 Types of Plots
09:3210.007 Draw a pair plot using seaborn library
10.008 Assignment 01 Demo
02:2310.009 Analysing Cause of Death
10.010 Assignment 02 Demo
01:4710.11 Quiz
10.012 Key Takeaways
00:59
Lesson 11 - Web Scraping with BeautifulSoup
52:26Preview11.001 Web Scraping and Parsing
12:5011.2 Knowledge Check
11.003 Understanding and Searching the Tree
12:5611.4 Navigating options
11.005 Demo3 Navigating a Tree
04:2211.6 Knowledge Check
11.007 Modifying the Tree
05:3711.008 Parsing and Printing the Document
09:0511.009 Web Scraping of Simplilearn Website
11.010 Assignment 01 Demo
01:5511.011 Web Scraping of Simplilearn Website Resource page
11.012 Assignment 02 demo
04:5711.13 Quiz
11.014 Key takeaways
00:44
Lesson 12 - Python integration with Hadoop MapReduce and Spark
40:39Preview12.001 Why Big Data Solutions are Provided for Python
04:5512.2 Hadoop Core Components
12.003 Python Integration with HDFS using Hadoop Streaming
07:2012.004 Demo 01 - Using Hadoop Streaming for Calculating Word Count
08:5212.5 Knowledge Check
12.006 Python Integration with Spark using PySpark
07:4312.007 Demo 02 - Using PySpark to Determine Word Count
04:1212.8 Knowledge Check
12.009 Determine the wordcount
12.010 Assignment 01 Demo
02:4712.011 Display all the airports based in New York using PySpark
12.012 Assignment 02 Demo
03:3012.13 Quiz
12.014 Key takeaways
01:20
Practice Projects
IBM HR Analytics Employee Attrition Modeling.
- Free Course
Data Science in Real life
PreviewLesson 1 - Course Objective
Learning Objectives
Lesson 2 - Defining Data Science
12:46PreviewLearning Objectives
1.1 What is data science
02:371.2 There are many paths to data science
03:551.3 Any advice for new data scientist
02:591.4 What is the cloud
03:15
Lesson 3 - What do Data Science People do
11:24Learning Objectives
2.1 A day in the life of a data science person
03:532.2 R versus Python
01:512.3 Data science tools and technology
05:40
Lesson 4 - Data Science in Business
10:40PreviewLearning Objectives
3.1 How should companies get started in data science
03:003.2 Recruiting for data science
07:40
Lesson 5 - Use Cases for Data Science
06:28Learning Objectives
4.1 Applications of data science
06:28
Lesson 6 - Data Science People
01:05PreviewLearning Objectives
5.1 Things data science people say
01:05Unlocking IBM Certificate
- Free Course
Python for Data Science
PreviewLesson 1 - Welcome
02:28PreviewWelcome
02:28Learning Objectives
Lesson 2 - Python Basics
11:55Preview2.1 Learning Objectives
2.2 Your first program
01:152.3 Types
02:572.4 Expressions and Variables
03:502.5 Write your First Python Code
2.6 String Operations
03:532.7 String Operations
Lesson 3 - Python Data Structures
16:223.1 Learning Objectives
3.2 Lists and Tuples
08:463.3 Lists and Tuples
3.4 Sets
05:123.5 Sets
3.6 Dictionaries
02:243.7 Dictionaries
Lesson 4 - Python Programming Fundamentals
41:08Preview4.1 Learning Objectives
4.2 Conditions and Branching
10:134.3 Conditions and Branching
4.4 Loops
06:404.5 Loops
4.6 Functions
13:284.7 Functions
4.8 Objects and Classes
10:474.9 Objects and Classes
Lesson 5 - Working with Data in Python
12:355.1 Learning Objectives
5.2 Reading files with open
03:385.3 Reading Files
5.4 Writing files with open
02:495.5 Writing Files
5.6 Loading data with Pandas
04:075.7 Working with and Saving data with Pandas
02:015.8 Loading Data and Viewing Data
Lesson 6 - Working with Numpy Arrays
18:26Preview6.1 Learning Objectives
6.2 Numpy One-Dimensional Arrays
11:186.3 Working with One-Dimensional Numpy Arrays
6.4 Numpy Two-Dimensional Arrays
07:086.5 Working with Two-Dimensional Numpy Arrays
Lesson 7 - Course Summary
01:13Course Summary
01:13Unlocking IBM Certificate
- Free Course
Statistics Essential for Data Science
PreviewLesson 1 Introduction
02:55Preview1.1 Introduction
02:55
Lesson 2 Sample or population data
03:56Preview2.1 Sample or population data
03:56
Lesson 3 The fundamentals of descriptive statistics
21:18Preview3.1 The fundamentals of descriptive statistics
03:183.2 Levels of measurement
02:573.3 Categorical variables. Visualization techniques for categorical variables
04:063.4 Numerical variables. Using a frequency distribution table
03:243.5 Histogram charts
02:273.6 Cross tables and scatter plots
05:06
Lesson 4 Measures of central tendency, asymmetry, and variability
25:174.1 Measures of central tendency, asymmetry, and variability
04:244.2 Measuring skewness
02:434.3 Measuring how data is spread out calculating variance
05:584.4 Standard deviation and coefficient of variation
04:544.5 Calculating and understanding covariance
03:314.6 The correlation coefficient
03:47
Lesson 5 Practical example descriptive statistics
14:305.1 Practical example descriptive statistics
14:30
Lesson 6 Distributions
16:17Preview6.1 Distributions
01:026.2 What is a distribution
03:406.3 The Normal distribution
03:456.4 The standard normal distribution
02:516.5 Understanding the central limit theorem
03:406.6 Standard error
01:19
Lesson 7 Estimators and Estimates
23:36Preview7.1 Estimators and Estimates
02:367.2 Confidence intervals - an invaluable tool for decision making
06:317.3 Calculating confidence intervals within a population with a known variance
02:307.4 Student’s T distribution
03:147.5 Calculating confidence intervals within a population with an unknown variance
04:077.6 What is a margin of error and why is it important in Statistics
04:38
Lesson 8 Confidence intervals advanced topics
14:27Preview8.1 Confidence intervals advanced topics
04:478.2 Calculating confidence intervals for two means with independent samples (part One)
04:368.3 Calculating confidence intervals for two means with independent samples (part two)
03:408.4 Calculating confidence intervals for two means with independent samples (part three)
01:24
Lesson 9 Practical example inferential statistics
09:379.1 Practical example inferential statistics
09:37
Lesson 10 Hypothesis testing Introduction
12:36Preview10.1 Hypothesis testing Introduction
04:5610.2 Establishing a rejection region and a significance level
04:2010.3 Type I error vs Type II error
03:20
Lesson 11 Hypothesis testing Let's start testing!
26:39Preview11.1 Hypothesis testing Let's start testing!
06:0711.2 What is the p-value and why is it one of the most useful tool for statisticians
03:5511.3 Test for the mean. Population variance unknown
04:2611.4 Test for the mean. Dependent samples
04:4511.5 Test for the mean. Independent samples (Part One)
03:3811.6 Test for the mean. Independent samples (Part Two)
03:48
Lesson 12 Practical example hypothesis testing
06:3112.1 Practical example hypothesis testing
06:31
Lesson 13 The fundamentals of regression analysis
18:32Preview13.1 The fundamentals of regression analysis
01:0213.2 Correlation and causation
04:0613.3 The linear regression model made easy
05:0213.4 What is the difference between correlation and regression
01:2813.5 A geometrical representation of the linear regression model
01:1813.6 A practical example - Reinforced learning
05:36
Lesson 14 Subtleties of regression analysis
23:25Preview14.1 Subtleties of regression analysis
02:0414.2 What is Rsquared and how does it help us
05:0014.3 The ordinary least squares setting and its practical applications
02:0814.4 Studying regression tables
04:3414.5 The multiple linear regression model
02:4214.6 Adjusted R-squared
04:5714.7 What does the F-statistic show us and why we need to understand it
02:00
Lesson 15 Assumptions for linear regression analysis
19:16Preview15.1 Assumptions for linear regression analysis
02:1115.2 Linearity
01:4015.3 No endogeneity
03:4315.4 Normality and homoscedasticity
05:0915.5 No autocorrelation
03:1115.6 No multicollinearity
03:22
Lesson 16 Dealing with categorical data
05:2016.1 Dealing with categorical data
05:20
Lesson 17 Practical example regression analysis
14:4217.1 Practical example regression analysis
14:42
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.
- 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.
- 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.
- 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.
- 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.
- 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.
Data Science with Python Exam & Certification
How do I earn my Simplilearn certificate?
To become a Certified Data Scientist with Python, you must fulfil the following criteria:- Complete one project out of the two provided in the course. Submit the deliverables of the project in the LMS which will be evaluated by our lead trainer
- Score a minimum of 60% in any one of the two simulation tests
- Complete 85% of the course
- Attend one complete batch.
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
Aashish Kumar
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
The sessions are very interesting and easy to understand. I enjoyed each and every one of them, thanks to the trainer, Prashant.
C Muthu Raman
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
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!!
Mukesh Pandey
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
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.
Akash Raj
Technology EngineerThe 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.
Shiv Sharma
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.
Shweta Chauhan
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.
Kiran Kumar
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.
Rajesh Dubey
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
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!
Jatin Alwani
Student at Lovely Professional UniversityI 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
Analyst at AccentureI 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.
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
Data Science with Python Training FAQs
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.
What are the system requirements?
To run Python, your system must fulfill the following basic requirements:- 32 or 64-bit Operating System
- 1GB RAM
The instruction uses Anaconda and Jupyter notebooks. The e-learning videos provide detailed instruction on how to install them.Who are our instructors and how are they selected?
All of our highly qualified trainers are industry experts with at least 10-12 years of relevant teaching experience. Each of them has gone through a rigorous selection process that includes profile screening, technical evaluation, and a training demo before they are certified to train for us. We also ensure that only those trainers with a high alumni rating remain on our faculty.What are the modes of training offered for this Python for Data Science course?
Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the course remotely from your desktop via video conferencing to enhance your productivity and reduce the time spent away from work or home.
Online Self-Learning: In this mode, you will receive lecture videos and can proceed through the course at your convenience.
WinPython portable distribution is the open source environment on which all hands-on exercises will be performed. Instructions for installation will be given during the training.What if I miss a class?
Simplilearn provides recordings of each class so you can review them as needed before the next session.
Our London Correspondence / Mailing address
Kemp House, 152 - 160 City Road, London EC1V 2NX, United Kingdom
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- Disclaimer
- PMP, PMI, PMBOK, CAPM, PgMP, PfMP, ACP, PBA, RMP, SP, and OPM3 are registered marks of the Project Management Institute, Inc.