Data Science Skills you will learn

  • 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

Who should learn Data Science with Python

  • Analytics Professionals
  • Software Professionals
  • IT Professionals
  • Data Scientist
  • Data Analyst

What you will learn in Data Science with Python

  • Data Science with Python

    • Lesson 01: Course Introduction

      08:57
      • 1.01 Course Introduction
        05:46
      • 1.02 Demo: Jupyter Lab Walk - Through
        03:11
    • Lesson 02: Introduction to Data Science

      09:10
      • 2.01 Learning Objectives
        00:27
      • 2.02 Data Science Methodology
        01:20
      • 2.03 From Business Understanding to Analytic Approach
        01:02
      • 2.04 From Requirements to Collection
        01:06
      • 2.05 From Understanding to Preparation
        01:10
      • 2.06 From Modeling to Evaluation
        01:53
      • 2.07 From Deployment to Feedback
        01:52
      • 2.08 Key Takeaways
        00:20
    • Lesson 03: Python Libraries for Data Science

      01:15:08
      • 3.01 Learning Objectives
        00:37
      • 3.02 Python Libraries for Data Science
        01:51
      • 3.03 Import Library into Python Program
        01:05
      • 3.04 Numpy
        04:35
      • 3.05 Demo: Numpy
        05:08
      • 3.06 Pandas
        04:12
      • 3.07 Series
        07:57
      • 3.08 DataFrame
        05:37
      • 3.09 Demo Pandas: Part One
        04:51
      • 3.10 Demo Pandas: Part Two
        02:59
      • 3.11 Matplotlib
        06:04
      • 3.12 Demo: Matplotlib
        02:09
      • 3.13 SciPy
        05:23
      • 3.14 Demo: Scipy
        01:38
      • 3.15 Scikit learn
        02:18
      • 3.16 Demo: Scikit learn
        06:20
      • 3.17 Web Scraping with Beautiful Soup
        06:23
      • 3.18 Parser
        03:07
      • 3.19 Demo: Web Scraping with Beautiful Soup
        02:20
      • 3.20 Key Takeaways
        00:34
    • Lesson 04: Data Wrangling

      31:32
      • 4.01 Learning Objectives
        00:42
      • 4.02 Data Exploration Loading Files: Part A
        02:53
      • 4.03 Data Exploration Loading Files: Part B
        01:36
      • 4.04 Data Exploration Techniques: Part A
        02:44
      • 4.05 Data Exploration Techniques: Part B
        02:48
      • 4.06 Seaborn
        02:19
      • 4.07 Demo: Correlation Analysis
        02:38
      • 4.08 Data Wrangling
        01:28
      • 4.09 Missing Values in a Dataset
        01:57
      • 4.10 Outlier Values in a Dataset
        01:50
      • 4.11 Demo: Outlier and Missing Value Treatment
        04:12
      • 4.12 Data Manipulation
        00:49
      • 4.13 Functionalities of Data Object in Python: Part A
        01:50
      • 4.14 Functionalities of Data Object in Python: Part B
        01:34
      • 4.15 Different Types of Joins
        01:34
      • 4.16 Key Takeaways
        00:38
    • Lesson 05: Feature Engineering

      07:03
      • 5.01 Learning Objectives
        00:28
      • 5.02 Introduction to Feature Engineering
        01:50
      • 5.03 Encoding of Catogorical Variables
        00:27
      • 5.04 Label Encoding
        01:46
      • 5.05 Techniques used for Encoding variables
        02:11
      • 5.06 Key Takeaways
        00:21
    • Lesson 06: Exploratory Data Analysis

      24:58
      • 6.01 Learning Objectives
        00:33
      • 6.02 Types of Plots
        09:38
      • 6.03 Plots and Subplots
        10:06
      • 6.04 Assignment 01: Pairplot Demo
        02:28
      • 6.05 Assignment 02: Pie Chart Demo
        01:52
      • 6.06 Key Takeaways
        00:21
    • Lesson 07: Feature Selection

      28:08
      • 7.01 Learning Objectives
        00:34
      • 7.02 Feature Selection
        01:28
      • 7.03 Regression
        00:54
      • 7.04 Factor Analysis
        01:58
      • 7.05 Factor Analysis Process
        01:07
      • 7.06 Principal Component Analysis (PCA)
        02:32
      • 7.07 First Principal Component
        02:44
      • 7.08 Eigenvalues and PCA
        02:33
      • 7.09 Demo: Feature Reduction
        05:36
      • 7.10 Linear Discriminant Analysis
        02:28
      • 7.11 Maximum Separable Line
        00:45
      • 7.12 Find Maximum Separable Line
        03:12
      • 7.13 Demo: Labeled Feature Reduction
        01:55
      • 7.14 Key Takeaways
        00:22
    • Practice Project

      • IBM HR Analytics Employee Attrition Modeling

Get a Completion Certificate

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Getting Started with Data Science with Python

Why you should learn Data Science with Python

46% of jobs

In the data science field require Python

1581% by 2020

Growth in demand for data science professionals

Career Opportunities

FAQs

  • What are the prerequisites to attend the Data Science with Python program?

    Learners should have a basic understanding of mathematics concepts like statistics, calculus, linear algebra, and probability before taking this Data Science with Python program. Knowledge of any programming language is also beneficial.

  • How long does it take to get started with Data Science with Python?

    The Data Science with Python program gives you access to 8 hours of in-depth learning material that you can follow at your own pace. It is important to practice Python programming to gain a strong hold in data science and this program will help you do it in a short time.

  • How do beginners learn Data Science with Python?

    Beginners always look for a step-by-step guide when they want to learn data science. While online tutorials and books are good to begin with, this Data Science with Python basics program helps you learn everything from scratch.

  • What is the Data Science with Python program?

    This Data Science with Python program is the ideal stepping stone in your learning journey as an aspiring data scientist. It will give you an understanding of data analytics tools and techniques, data analysis, visualization, Python basics and its libraries, web scraping, and natural language processing. 

  • What should I learn first in Data Science?

    You can begin learning Data Science by understanding the data analytics process, data types, and statistical analysis. You can then go ahead with Python programming fundamentals.

  • Is the Data Science with Python program easy to learn?

    The instructors who have designed this Data Science with Python program have rich teaching experience and are aware of the various learner needs. As such, professionals who don’t have any prior knowledge of data science can still get started easily with Python through this program.

  • What are the basics in a Data Science with Python program?

    Exploratory data analytics, data types and plotting, statistical analysis process, and data manipulation are the basics covered in this Data Science with Python program.

  • What is Data Science used for?

    The importance of data science has increased significantly over the past few years. Companies are using data science to convert raw data into meaningful information through effective processing, analysis, modeling, and visualization. By uncovering such patterns and insights from data, they are able to make more informed decisions and serve customers better.

  • Why should you learn Data Science with Python?

    Data science is the hottest technology of the digital age. Python is one of the most popular languages in data science, which is used to perform data analysis, data manipulation, and data visualization. Getting started with Python is one of the primary steps in your journey to become a data scientist which is one of the top ranking professionals in any analytics organization. Despite numerous lucrative opportunities for skilled data professionals, there aren’t enough data scientists. Because of this, now would be the right time to learn Data Science with Python. 

  • What are 5 essential steps to learn Data Science with Python?

    The 5 steps that any aspirant should follow to learn Data Science with Python include:
    Step 1: Master the fundamentals of Python
    Step 2: Build multiple Python projects to fine-tune your skills
    Step 3: Learn Python libraries like NumPy, Pandas, and Matplotlib
    Step 4: Develop a versatile data science portfolio
    Step 5: Enroll in advanced data science programs to excel further

  • What are the top skills required for a career in Data Science?

     The top skills required for a career in data science include:

    • Knowledge of various programming languages, such as Python, Perl, C/C++, SQL, and Java
    • An understanding of SAS and other analytical tools
    • Adept at working with unstructured data
    • A sharp business acumen
    • Excellent communication skills
    • Strong intuition regarding data

  • Can I complete this Data Science with Python program in 90 days?

    The lessons covered in this Data Science with Python program have rich content and are easy to follow. Learners can study at their own pace and complete the free course quite earlier than 90 days.

  • Will I get a certificate after completing the Data Science with Python program?

    Yes, You will receive a Course Completion Certificate from SkillUp upon completing the Data Science with Python free program. You can unlock it by logging in to your SkillUp account. As soon as the certificate is unlocked, you will receive a mail with a link to your SkillUp learning dashboard on your registered mail address. Click the link to view and download your certificate. You can even add the certificate to your resume and share it on social media platforms.

  • What are the career opportunities in Data Science?

    The world is wide open for professionals willing to start a career in data science. Those who gain data science skills through this program can aim for designations like data scientists, data architects, data analysts, Python programmers, and more. You’ll find a lot of open positions related to these job roles when you search in any job portal.

  • What are my next best learning options after completing this Data Science with Python program?

    After completing this Data Science with Python free course, you can enroll in other courses like Data Scientist Master’s Program or Post Graduate Program in Data Science.

Learner Review

  • Ashish KC Khatri

    Ashish KC Khatri

    I learned some new interesting python content, from Simplilearn's Data Science course. Looking forward to learn more.

  • Abhimanyu Chandgude

    Abhimanyu Chandgude

    Thank you Simplilearn for providing such an amazing and valuable course!

  • Mohit

    Mohit

    3rd year ECE(B.E) , PUSSGRC,Hoshiarpur,

    The Data Science with Python courses helped me a lot in improving my understanding of Python skills. I really enjoyed learning it.

  • Kipngetich Evans

    Kipngetich Evans

    The course is well-structured. I loved learning this course because it introduced me to a whole new world of Data Science.

  • Pooja Rohiwal

    Pooja Rohiwal

    The entire syllabus for this course was explained well. The best part was the exercises which helped a lot in understanding python better.

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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.
  • *According to Simplilearn survey conducted and subject to terms & conditions with Ernst & Young LLP (EY) as Process Advisors