Data Science with Python Certification Training Course
This Data Science with Python certification course gives you a complete overview of Python’s data analytics tools and techniques. Learning python is a crucial skill for many data science roles, and you can develop it with this Python data science course. With a blended learning approach, you can learn Python for data science along with concepts like data wrangling, mathematical computing, and more. Unlock your career as a data scientist with "Data Science with Python Certification Training Course"
Data Science with Python Certification Training Course
The Data Science with Python course teaches you to master the concepts of Python programming. Through this Data Science with python certification training, you will learn Data Analysis, Machine Learning, Data Visualization, Web Scraping, & NLP. Upon course completion, you will master the essential Data Science tools using Python.
Training 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
- ScikitLearn package for Natural Language Processing
Benefits
Data Science is an evolving field and Python has become a required skill for 46-percent of jobs in Data Science. According to the US Bearue of Labor Statistics around 11.6 million data science jobs will be created by 2026 and professionals with Python skills will have an additional advantage.
Training Options
SELF-PACED LEARNING
- Lifetime access to high-quality self-paced eLearning content curated by industry experts
- 4 hands-on projects to perfect the skills learnt
- 3 simulation test papers for self-assessment
- Lab access to practice live during sessions
- 24x7 learner assistance and support
No exam voucher.
ONLINE BOOTCAMP +
- Everything in Self-Paced Learning, plus
- 90 days of flexible access to online classes
- Live, online classroom training by top instructors and practitioners
Exam voucher included.
Course Curriculum
- 0.1 - Course Overview
- 1.1 - Introduction to Data Science
- 1.2 - Different Sectors Using Data Science
- 1.3 - Purpose and Components of Python
- 1.4 - Quiz
- 1.5 - Key Takeaways
- 2.1 - Data Analytics Process
- 2.2 - Knowledge Check
- 2.3 - Exploratory Data Analysis(EDA)
- 2.4 - EDA-Quantitative Technique
- 2.5 - EDA - Graphical Technique
- 2.6 - Data Analytics Conclusion or Predictions
- 2.7 - Data Analytics Communication
- 2.8 - Data Types for Plotting
- 2.9 - Data Types and Plotting
- 2.10 - Knowledge Check
- 2.11 - Quiz
- 2.12 - Key Takeaways
- 3.1 - Introduction to Statistics
- 3.2 - Statistical and Non-statistical Analysis
- 3.3 - Major Categories of Statistics
- 3.4 - Statistical Analysis Considerations
- 3.5 - Population and Sample
- 3.6 - Statistical Analysis Process
- 3.7 - Data Distribution
- 3.8 - Dispersion
- 3.9 - Knowledge Check
- 3.10 - Histogram
- 3.11 - Knowledge Check
- 3.12 - Testing
- 3.13 - Knowledge Check
- 3.14 - Correlation and Inferential Statistics
- 3.15 - Quiz
- 3.16 - Key Takeaways
- 4.1 - Anaconda
- 4.2 - Installation of Anaconda Python Distribution (contd.)
- 4.3 - Data Types with Python
- 4.4 - Basic Operators and Functions
- 4.5 - Quiz
- 4.6 - Key Takeaways
- 5.1 - Introduction to Numpy
- 5.2 - Activity-Sequence it Right
- 5.3 - Demo 01-Creating and Printing an ndarray
- 5.4 - Knowledge Check
- 5.5 - Class and Attributes of ndarray
- 5.6 - Basic Operations
- 5.7 - Activity-Slice It
- 5.8 - Copy and Views
- 5.9 - Mathematical Functions of Numpy
- 5.10 - Analyse GDP of Countries
- 5.11 - Assignment 01 Demo
- 5.12 - Analyse London Olympics Dataset
- 5.13 - Assignment 02 Demo
- 5.14 - Quiz
- 5.145- Key Takeaways
- 6.1 - Introduction to SciPy
- 6.2 - SciPy Sub Package - Integration and Optimization
- 6.3 - Knowledge Check
- 6.4 - SciPy sub package
- 6.5 - Demo - Calculate Eigenvalues and Eigenvector
- 6.6 - Knowledge Check
- 6.7 - SciPy Sub Package - Statistics, Weave and IO
- 6.8 - Solving Linear Algebra problem using SciPy
- 6.9 - Assignment 01 Demo
- 6.10 - Perform CDF and PDF using Scipy
- 6.11 - Assignment 02 Demo
- 6.12 - Quiz
- 6.13 - Key Takeaways
- 7.1 - Introduction to Pandas
- 7.2 - Knowledge Check
- 7.3 - Understanding DataFrame
- 7.4 - View and Select Data Demo
- 7.5 - Missing Values
- 7.6 - Data Operations
- 7.7 - Knowledge Check
- 7.8 - File Read and Write Support
- 7.9 - Knowledge Check-Sequence it Right
- 7.10 - Pandas Sql Operation
- 7.11 - Analyse the Federal Aviation Authority Dataset using Pandas
- 7.12 - Assignment 01 Demo
- 7.13 - Analyse NewYork city fire department Dataset
- 7.14 - Assignment 02 Demo
- 7.15 - Quiz
- 7.16 - Key Takeaways
- 8.1 - Machine Learning Approach
- 8.2 - Steps One and Two
- 8.3 - Steps Three and Four
- 8.4 - How it Works
- 8.5 - Steps Five and Six
- 8.6 - Supervised Learning Model Considerations
- 8.7 - ScikitLearn
- 8.8 - Supervised Learning Models - Linear Regression
- 8.9 - Supervised Learning Models - Logistic Regression
- 8.10 - Unsupervised Learning Models
- 8.11 - Pipeline
- 8.12 - Model Persistence and Evaluation
- 8.13 - Knowledge Check
- 8.14 - Analysing Ad Budgets for different media channels
- 8.15 - Assignment One
- 8.16 - Building a model to predict Diabetes
- 8.17 - Assignment Two
- 8.18 - Knowledge Check
- 8.19 - Key Takeaways
- 9.1 - NLP Overview
- 9.2 - NLP Applications
- 9.3 - Knowledge Check
- 9.4 - NLP Libraries-Scikit
- 9.5 - Extraction Considerations
- 9.6 - Scikit Learn-Model Training and Grid Search
- 9.7 - Analysing Spam Collection Data
- 9.8 - Demo Assignment 01
- 9.9 - Sentiment Analysis using NLP
- 9.10 - Demo Assignment 02
- 9.11 - Quiz
- 9.12 - Key Takeaways
- 10.1 - Introduction to Data Visualization
- 10.2 - Knowledge Check
- 10.3 - Line Properties
- 10.4 - (x,y) Plot and Subplots
- 10.5 - Knowledge Check
- 10.6 - Types of Plots
- 10.7 - Draw a pair plot using seaborn library
- 10.8 - Assignment 01 Demo
- 10.9 - Analysing Cause of Death
- 10.10 - Assignment 02 Demo
- 10.11 - Quiz
- 10.12 - Key Takeaways
- 11.1 - Web Scraping and Parsing
- 11.2 - Knowledge Check
- 11.3 - Understanding and Searching the Tree
- 11.4 - Navigating options
- 11.5 - Demo3 Navigating a Tree
- 11.6 - Knowledge Check
- 11.7 - Modifying the Tree
- 11.8 - Parsing and Printing the Document
- 11.9 - Web Scraping of Simplilearn Website
- 11.10 - Assignment 01 Demo
- 11.11 - Web Scraping of Simplilearn Website Resource page
- 11.12 - Assignment 02 demo
- 11.13 - Quiz
- 11.14 - Key takeaways
- 12.1 - Why Big Data Solutions are Provided for Python
- 12.2 - Hadoop Core Components
- 12.3 - Python Integration with HDFS using Hadoop Streaming
- 12.4 - Demo 01 - Using Hadoop Streaming for Calculating Word Count
- 12.5 - Knowledge Check
- 12.6 - Python Integration with Spark using PySpark
- 12.7 - Demo 02 - Using PySpark to Determine Word Count
- 12.8 - Knowledge Check
- 12.9 - Determine the wordcount
- 12.10 - Assignment 01 Demo
- 12.11 - Display all the airports based in New York using PySpark
- 12.12 - Assignment 02 Demo
- 12.13 - Quiz
- 12.14 - Key takeaways
- IBM HR Analytics Employee Attrition Modeling.
- 1.1 - Math Refresher
- 1.1 - Course Introduction
- 1.2 - What Will You Learn
- 2.1 - Learning Objectives
- 2.2 - What Is Statistics
- 2.3 - Why Statistics
- 2.4 - Difference between Population and Sample
- 2.5 - Different Types of Statistics
- 2.6 - Importance of Statistical Concepts in Data Science
- 2.7 - Application of Statistical Concepts in Business
- 2.8 - Case Studies of Statistics Usage in Business
- 2.9 - Recap
- 3.1 - Learning Objectives
- 3.2 - Types of Data in Business Contexts
- 3.3 - Data Categorization and Types of Data
- 3.4 - Types of Data Collection
- 3.5 - Types of Data
- 3.6 - Structured vs. Unstructured Data
- 3.7 - Sources of Data
- 3.8 - Data Quality Issues
- 3.9 - Recap
- 4.1 - Learning Objectives
- 4.2 - Mathematical and Positional Averages
- 4.3 - Measures of Central Tendancy: Part A
- 4.4 - Measures of Central Tendancy: Part B
- 4.5 - Measures of Dispersion
- 4.6 - Range Outliers Quartiles Deviation
- 4.7 - Mean Absolute Deviation (MAD) Standard Deviation Variance
- 4.8 - Z Score and Empirical Rule
- 4.9 - Coefficient of Variation and Its Application
- 4.10 - Measures of Shape
- 4.11 - Summarizing Data
- 4.12 - Recap
- 4.13 - Case Study One: Descriptive Statistics
- 5.1 - Learning Objectives
- 5.2 - Data Visualization
- 5.3 - Basic Charts
- 5.4 - Advanced Charts
- 5.5 - Interpretation of the Charts
- 5.6 - Selecting the Appropriate Chart
- 5.7 - Charts Do's and Dont's
- 5.8 - Story Telling With Charts
- 5.9 - Recap
- 5.10 - Case Study Two: Data Visualization
- 6.1 - Learning Objectives
- 6.2 - Introduction to Probability
- 6.3 - Key Terms in Probability
- 6.4 - Conditional Probability
- 6.5 - Types of Events: Independent and Dependent
- 6.6 - Addition Theorem of Probability
- 6.7 - Multiplication Theorem of Probability
- 6.8 - Bayes Theorem
- 6.9 - Recap
- 7.1 - Learning Objectives
- 7.2 - Random Variable
- 7.3 - Probability Distributions Discrete vs.Continuous: Part A
- 7.4 - Probability Distributions Discrete vs.Continuous: Part B
- 7.5 - Commonly Used Discrete Probability Distributions: Part A
- 7.6 - Discrete Probability Distributions: Poisson
- 7.7 - Binomial by Poisson Theorem
- 7.8 - Commonly Used Continuous Probability Distribution
- 7.9 - Applicaton of Normal Distribution
- 7.10 - Recap
- 8.1 - Learnning Objectives
- 8.2 - Introduction to Sampling and Sampling Errors
- 8.3 - Advantages and Disadvantages of Sampling
- 8.4 - Probability Sampling Methods: Part A
- 8.5 - Probability Sampling Methods: Part B
- 8.6 - Non-Probability Sampling Methods: Part A
- 8.7 - Non-Probability Sampling Methods: Part B
- 8.8 - Uses of Probability Sampling and Non-Probability Sampling
- 8.9 - Sampling
- 8.10 - Probability Distribution
- 8.11 - Theorem Five Point One
- 8.12 - Center Limit Theorem
- 8.13 - Recap
- 8.14 - Case Study Three: Sample and Sampling Techniques
- 8.15 - Spotlight
- 9.1 - Learning Objectives
- 9.2 - Hypothesis and Hypothesis Testing in Businesses
- 9.3 - Null and Alternate Hypothesis
- 9.4 - P Value
- 9.5 - Levels of Significance
- 9.6 - Type One and Two Errors
- 9.7 - Z Test
- 9.8 - Confidence Intervals and Percentage Significance Level: Part A
- 9.9 - Confidence Intervals: Part B
- 9.10 - One Tail and Two Tail Tests
- 9.11 - Notes to Remember for Null Hypothesis
- 9.12 - Alternate Hypothesis
- 9.13 - Recap
- 9.14 - Case Study Four: Inferential Statistics
- Hypothesis Testing
- 10.1 - Learning Objectives
- 10.2 - Bivariate Analysis
- 10.3 - Selecting the Appropriate Test for EDA
- 10.4 - Parametric vs. Non-Parametric Tests
- 10.5 - Test of Significance
- 10.6 - Z Test
- 10.7 - T Test
- 10.8 - Parametric Tests ANOVA
- 10.9 - Chi-Square Test
- 10.10 - Sign Test
- 10.11 - Kruskal Wallis Test
- 10.12 - Mann Whitney Wilcoxon Test
- 10.13 - Run Test for Randomness
- 10.14 - Recap
- 11.1 - Learning Objectives
- 11.2 - Correlation
- 11.3 - Karl Pearson's Coefficient of Correlation
- 11.4 - Karl Pearsons: Use Cases
- 11.5 - Spearmans Rank Correlation Coefficient
- 11.6 - Causation
- 11.7 - Example of Regression
- 11.8 - Coefficient of Determination
- 11.9 - Quantifying Quality
- 11.10 - Recap
- 12.1 - Learning Objectives
- 12.2 - How to Use Statistics In Day to Day Business
- 12.3 - Example: How to Not Lie With Statistics
- 12.4 - How to Not Lie With Statistics
- 12.5 - Lying Through Visualizations
- 12.6 - Lying About Relationships
- 12.7 - Recap
- 12.8 - Spotlight
- 13.1 - Assisted Practice: Problem Statement
- 13.2 - Assisted Practice: Solution
Exams and Certifications
Once you successfully complete the Data Science with Python training, Simplilearn will provide you with an industry-recognized course completion certificate which will have a lifelong validity.
Online Classroom:
- Attend one complete batch of Data Science with Python training.
- Submit at least one completed project.
Online Self-Learning:
- Complete 85% of the course
- Submit at least one completed project.
Yes, we provide 1 practice test as part of our Data Science with Python 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.
Training FAQ
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.
Python is one of the most popular languages in Data Science, which can be used to perform data analysis, data manipulation, and data visualization. Python offers access to a wide variety of Data Science libraries and it is the ideal language for implementing algorithms and the rapid development of applications.
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 Python Data Science course 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.
Harvard Business Review has already named Data Scientist as the ‘Sexiest Job of the 21st Century.’ The statement is echoed in LinkedIn Emerging Jobs Report 2021 in which Data Science specialists are one of the top emerging jobs in the US with Python as one of its key skills. The job role has witnessed an annual growth of 35 percent for Data scientists and Data engineers.
If you have prior coding experience or familiarity with any other object-oriented programming language, it will be easier for you to learn Python. However, it is not compulsory.
Python has simple syntax and is easy to understand. Knowledge of Java or C++ language helps in learning Python faster. This is because Python is also object-oriented and many of its prototypes are similar to Java. So you can easily migrate to Python with this comprehensive course.
Python is used for a variety of applications and you don’t need to be familiar with all of its libraries and modules. Even if you know the basics of Python, this Data Science with Python certification covers the popular libraries of Python that are used in data science projects.
Yes, Python supports a lot of open-source libraries like SciPy, NumPy, Scikit-Learn, TensorFlow, Matplotlib, and Pandas.
Yes, our Data Science with Python course is specifically designed to impart industry-oriented skills. The course material, practice with integrated labs, and real-world projects enhance your practical knowledge and help you apply them to Data Science projects.
It is beneficial if you brush up your skills in core math, statistics, and programming basics to get started with this Data Science with Python course.
Major companies like Google, Instagram, Goldman Sachs, Facebook, Quora, Netflix, Dropbox, and PayPal use Python.
Data scientists handle a variety of tasks in their day-to-day routine. They gather, merge, and analyze data and identify trends and patterns. They also build and test new algorithms to simplify data problems. Python is used along with other tools to perform all these tasks.
Python is a high-level programming language with an enormous community. Its flexibility is quite useful for any issues related to application development. It has a rich set of libraries and frameworks that make it an excellent choice for Data Science like Pandas, NumPy, SciPy, Matplotlib.
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 instructions on how to install them.
All of our highly qualified Data Science 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.
Live Virtual Classroom or Online Classroom: In online classroom training, you have the convenience of attending the Python Data Science 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.
If you enroll in the self-paced e-learning training program, you will have access to pre-recorded videos. However, if you enroll for the Online Classroom Flexi-Pass, you will have access to both instructor-led Data Science with Python training conducted online as well as the pre-recorded videos.
Simplilearn provides recordings of each class so you can review them as needed before the next session.
Yes, you can cancel your enrollment if necessary. We will refund the course price after deducting an administration fee. To learn more, you can view our Refund Policy.
Yes, we have group discount packages for classroom training programs. Contact Help & Support to learn more about group discounts.
You can enroll for this Data Science with Python certification training on our website and make an online payment using any of the following options:
- Visa Credit or Debit Card
- MasterCard
- American Express
- Diner’s Club
- PayPal
Once payment is received you will automatically receive a payment receipt and access information via email.
Contact us using the form on the right of any page on the Simplilearn website, or select the Live Chat link. Our customer service representatives can provide you with more details.
Our teaching assistants are a dedicated team of subject matter experts here to help you get certified in Data Science on your first attempt. They engage students proactively to ensure the course path is being followed and help you enrich your learning experience, from class onboarding to project mentoring and job assistance. Teaching Assistance is available during business hours.
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 Python Data Science course with us.
You can either enroll in our Data Scientist Course or if you are looking to get a University certificate, you can enroll in the Post Graduate Program in Data Science.
* The projects have been built leveraging real publicly available data-sets of the mentioned organizations.
To become a data science expert, all you need is prior experience in mathematics or statistics and knowledge of programming languages like Python, Java, C++, etc. Simplilearn helps you gain expertise in Data Science with its Data Science with Python certification and have a successful career.
Data science collects relevant data, analyzes and interprets, and finds solutions for addressing business problems. Starting from healthcare to advertising, Data Science has applications in almost every possible field.
Not at all. Simplilearn’s Data Science with Python course has been tailored to meet the learning objectives of both beginners and experienced people and can be easily pursued by anyone meeting the course eligibility requirements.
Yes, Data Science is definitely a good career option given the following reasons:
- Data science is everywhere and expanding at an exponential rate! The market size of Data science has been projected to reach $178 billion by the end of 2025.
- As highlighted by the US Bureau of Labour Statistics (BLS), job roles requiring Data Science-related skills will likely surge by 2026.
- Data Scientists are among the highest-paid professionals earning an average salary of $1,49,982 per year.
While seeking data science with python training, beginners can first start with basics by completing the following fundamental modules included in the course:
- Python Basics
- Math Refresher
- Data Science in Real Life
- Statistics Essentials for Data Science
Upon developing a profound base in Data Science with Python, you can start with the course in the given order for a systematic learning experience.
Yes, seeking data science with python training is worth it because, with the help of this certification, you’ll be able to:
- Attain an in-depth understanding of data science processes, data wrangling, data exploration, data visualization, hypothesis building and testing, and the basics of statistics.
- Comprehend the essential concepts of Python programming such as data types, lists, tuples, dicts, basic level operators, and functions.
- Perform advanced level mathematical calculations utilizing the NumPy and SciPy packages, and their large library of mathematical functions.
- Carry analysis of data and manipulation using data structures and Pandas package tools
- Gain an in-depth understanding of supervised and unsupervised learning models, such as logistic regression, linear regression, data clustering, dimension reduction, K-NN, and pipeline.
- Use the Scikit-Learn package for NLP and matplotlib library of Python for data visualization.
After getting a data science with python certification, you can work as a:
- Business Analyst
- Database Administrator
- Big Data Engineer or Data Architect
- Data Analyst
- ML Engineer
- Business Intelligence (BI) Developer
- Business Intelligence Analyst
- Statistician
- Data Scientist
- Computer Vision(CV) Engineer
- Natural Language Processing (NLP) Engineer
- MLOps Engineer
A data science expert is primarily involved in collecting and analyzing data by utilizing various analytics and reporting tools to identify patterns, trends, and correlations in data sets. With the help of Simplilearn’s Data Science with Python certification, you will be able to gain a complete understanding of key roles and responsibilities of data science experts.
A data science expert should possess the following skills:
- Knowledge of programming languages like Python, R, and SQL
- Profound knowledge of statistics and related concepts
- Machine learning for handling big sets of data.
- Knowledge of Multivariable Calculus & Linear Algebra
- Data wrangling to refine data
- Knowledge of data visualization tools for easy communication of insights collected
Seeking data science with python certification will help you gain all the skills mentioned above and have a flourishing career in data science.
Data Science has applications in every possible industry; however, some industries use data science extensively, such as retail, healthcare, banking and finance, construction, transportation, communications, media, and entertainment, education, manufacturing, natural resources, and energy and utility. Upon completing Simplilearn’s data science with python course, which is highly career-oriented, you can easily find job opportunities in these industries.
Some of the top recruiters hiring professionals with data science with Python certification are HData Systems, Hyperlink InfoSystem, Tata Consultancy Services, Accenture, Tech Mahindra, Capgemini India Pvt Ltd, Tiger Analytics, Genpact, LatentView Analytics, and DataFactz.
To have a comprehensive data science with python training, you can consider referring to the following books:
- Python For Data Analysis written by Wes McKinney
- Automate The Boring Stuff With Python written by Al Sweigart
- Machine Learning with Python Cookbook written by Chris Albon
- Python Cookbook written by Brian K. Jones and David M. Beazley
- Hands-On Machine Learning with Scikit-Learn and TensorFlow written by Aurelien Geron
- Data Visualization in Python by Gilbert Tanner
On average, professionals with Data Science with Python certification earn an annual salary of $97853.