Please note, this is a STATIC archive of website www.simplilearn.com from 27 Mar 2023, cach3.com does not collect or store any user information, there is no "phishing" involved.
Simplilearn Live

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

      09:05
      • 1.01 Course Introduction
        05:54
      • 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

      01:59:39
      • 3.01 Learning Objectives
        00:34
      • 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 Fundamentals of Numpy
        02:13
      • 3.07 Numpy Array Shapes and axes Part A
        02:48
      • 3.08 Numpy Array Shapes and axes Part B
        03:22
      • 3.09 Arithmetic Operations
        02:34
      • 3.10 Conditional Statements in Python
        02:44
      • 3.11 Common Mathematical and Statistical Functions in NumPy
        04:25
      • 3.12 Indexing and Slicing in Python Part A
        02:26
      • 3.13 Indexing and Slicing in Python Part B
        02:25
      • 3.14 Introduction to Pandas
        01:41
      • 3.15 Introduction to Pandas Series
        03:37
      • 3.16 Querying a Series
        03:54
      • 3.17 Pandas Dataframe
        02:53
      • 3.18 Introduction to Pandas Panel
        01:45
      • 3.19 Common Functions in Pandas
        02:20
      • 3.20 Statistical Functions in Pandas
        01:43
      • 3.21 Date and Timedelta
        02:18
      • 3.22 IO Tools
        02:36
      • 3.23 Categorical Data
        02:09
      • 3.24 Working with Text Data
        02:34
      • 3.25 Iteration
        01:54
      • 3.26 Plotting with Pandas
        03:23
      • 3.27 Matplotlib
        06:04
      • 3.28 Demo Matplotlib
        02:09
      • 3.29 Data Visualization Libraries in Python Matplotlib
        01:30
      • 3.30 Graph Types
        01:14
      • 3.31 Using Matplotlib to Plot Graphs
        03:32
      • 3.32 Matplotlib for 3d Visualization
        02:14
      • 3.33 Using Matplotlib with Other Python Packages
        01:02
      • 3.34 Data Visualization Libraries in Python Seaborn An Introduction
        00:58
      • 3.35 Seaborn Visualization Features
        02:13
      • 3.36 Using Seaborn to Plot Graphs
        01:40
      • 3.37 Analysis using seaborn plots
        00:53
      • 3.38 Plotting 3D Graphs for Multiple Columns using Seaborn
        03:16
      • 3.39 SciPy
        05:23
      • 3.40 Demo Scipy
        01:38
      • 3.41 Scikit-learn
        02:08
      • 3.42 Scikit Models
        01:25
      • 3.43 Scikit Datasets
        01:12
      • 3.44 Preprocessing Data in Scikit Learn Part 1
        01:28
      • 3.45 Preprocessing Data in Scikit Learn Part 2
        01:45
      • 3.46 Preprocessing Data in Scikit Learn Part 3
        02:04
      • 3.47 Demo Scikit learn
        06:20
      • 3.48 Key Takeaways
        00:34

    • Lesson 04: Statistics

      02:29:57
      • 4.01 Learning Objectives
        00:34
      • 4.02 Introduction to Linear Algebra
        02:09
      • 4.03 Scalars and vectors
        01:27
      • 4.04 Dot product of Two Vectors
        02:02
      • 4.05 Linear Independence of Vectors
        00:46
      • 4.06 Norm of a Vector
        01:33
      • 4.07 Matrix
        02:46
      • 4.08 Matrix Operations
        02:38
      • 4.09 Transpose of a Matrix
        00:47
      • 4.10 Rank of a Matrix
        01:45
      • 4.11 Determinant of a matrix and Identity matrix or operator
        02:15
      • 4.12 Inverse of a matrix and Eigenvalues and Eigenvectors
        02:10
      • 4.13 Calculus in Linear Algebra
        01:14
      • 4.14 Importance of Statistics for Data Science
        02:00
      • 4.15 Common Statistical Terms
        01:19
      • 4.16 Types of Statistics
        02:10
      • 4.17 Data Categorization and types of data
        02:40
      • 4.18 Levels of Measurement
        02:04
      • 4.19 Measures of central tendency mean
        01:33
      • 4.20 Measures of Central Tendency Median
        01:37
      • 4.21 Measures of Central Tendency Mode
        01:03
      • 4.22 Measures of Dispersion
        01:56
      • 4.23 Variance
        02:14
      • 4.24 Random Variables
        01:36
      • 4.25 Sets
        02:03
      • 4.26 Measure of Shape Skewness
        01:38
      • 4.27 Measure of Shape Kurtosis
        01:20
      • 4.28 Covariance and corelation
        02:11
      • 4.29 Basic Statistics with Python Problem Statement
        00:49
      • 4.30 Basic Statistics with Python Solution
        10:30
      • 4.31 Probability its Importance and Probability Distribution
        02:49
      • 4.32 Probability Distribution Binomial Distribution
        02:13
      • 4.33 Binomial Distribution using Python
        01:31
      • 4.34 Probability Distribution Poisson Distribution
        02:08
      • 4.35 Poisson Distribution Using Python
        01:20
      • 4.36 Probability Distribution Normal Distribution
        03:17
      • 4.37 Probability Distribution Uniform Distribution
        01:03
      • 4.38 Probability Distribution Bernoulli Distribution
        02:27
      • 4.39 Probability Density Function and Mass Function
        01:57
      • 4.40 Cumulative Distribution Function
        01:52
      • 4.41 Central Limit Theorem
        02:22
      • 4.42 Bayes Theorem
        01:50
      • 4.43 Estimation Theory
        02:09
      • 4.44 Point Estimate using Python
        00:45
      • 4.45 Distribution
        01:11
      • 4.46 Kurtosis Skewness and Student's T- distribution
        01:46
      • 4.47 Hypothesis Testing and mechanism
        01:59
      • 4.48 Hypothesis Testing Outcomes Type I and II Errors
        01:28
      • 4.49 Null Hypothesis and Alternate Hypothesis
        01:27
      • 4.50 Confidence Intervals
        01:32
      • 4.51 Margin of Errors
        01:21
      • 4.52 Confidence Levels
        01:05
      • 4.53 T test and P values Using Python
        04:39
      • 4.54 Z test and P values Using Python
        05:25
      • 4.55 Comparing and Contrastin T test and Z-tests
        02:54
      • 4.56 Chi Squared Distribution
        02:32
      • 4.57 Chi Squared Distribution using Python
        03:18
      • 4.58 Chi squared Test and Goodness of Fit
        02:16
      • 4.59 ANOVA
        02:05
      • 4.60 ANOVA Terminologies
        01:31
      • 4.61 Assumptions and Types of ANOVA
        02:19
      • 4.62 Partition of Variance
        02:32
      • 4.63 F-distribution
        02:01
      • 4.64 F Distribution using Python
        03:54
      • 4.65 F-Test
        02:32
      • 4.66 Advanced Statistics with Python Problem Statement
        00:54
      • 4.67 Advanced Statistics with Python Solution
        10:06
      • 4.68 Key Takeaways
        00:38

    • Lesson 05: Data Wrangling

      31:32
      • 5.01 Learning Objectives
        00:42
      • 5.02 Data Exploration Loading Files Part A
        02:53
      • 5.03 Data Exploration Loading Files Part B
        01:36
      • 5.04 Data Exploration Techniques Part A
        02:44
      • 5.05 Data Exploration Techniques Part B
        02:48
      • 5.06 Seaborn
        02:19
      • 5.07 Demo Correlation Analysis
        02:38
      • 5.08 Data Wrangling
        01:28
      • 5.09 Missing Values in a Dataset
        01:57
      • 5.10 Outlier Values in a Dataset
        01:50
      • 5.11 Demo Outlier and Missing Value Treatment
        04:12
      • 5.12 Data Manipulation
        00:49
      • 5.13 Functionalities of Data Object in Python Part A
        01:50
      • 5.14 Functionalities of Data Object in Python Part B
        01:34
      • 5.15 Different Types of Joins
        01:34
      • 5.16 Key Takeaways
        00:38

    • Lesson 06: Feature Engineering

      06:57
      • 6.01 Learning Objectives
        00:28
      • 6.02 Introduction to Feature Engineering
        01:50
      • 6.03 Encoding of Catogorical Variables
        00:27
      • 6.04 Label Encoding
        01:46
      • 6.05 Techniques used for Encoding variables
        02:11
      • 6.06 Key Takeaways
        00:15

    • Lesson 07: Exploratory Data Analysis

      24:58
      • 7.01 Learning Objectives
        00:33
      • 7.02 Types of Plots
        09:38
      • 7.03 Plots and Subplots
        10:06
      • 7.04 Assignment 01 Pairplot Demo
        02:28
      • 7.05 Assignment 02 Pie Chart Demo
        01:52
      • 7.06 Key Takeaways
        00:21

    • Lesson 08: Feature Selection

      06:15
      • 8.01 Learning Objectives
        00:33
      • 8.02 Feature Selection
        01:28
      • 8.03 Regression
        00:54
      • 8.04 Factor Analysis
        01:58
      • 8.05 Factor Analysis Process
        01:07
      • 8.06 Key Takeaways
        00:15

Get a Completion Certificate

Share your certificate with prospective employers and your professional network on LinkedIn.

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 is Data Science so popular?

    Businesses are going through digital transformation like never before. It is now believed that traditional skills are soon going to be replaced with digital skills. Data science is one such evolving field where professionals with specialized skills are finding good career opportunities. The demand for roles like data scientists, data analysts, and data engineers have increased considerably, making data science a popular career domain. 

  • 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 Course or PG 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.

prevNext

Related Courses

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