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Data Analysis Courses for Beginners

Beginner data analysis courses can help you learn data collection techniques, basic statistical concepts, data visualization, and interpretation of results. You can build skills in using spreadsheets for data manipulation, creating charts for visual representation, and applying descriptive statistics to summarize findings. Many courses introduce tools like Excel, Google Sheets, and data visualization software such as Tableau, showing you how to effectively analyze and present data to support decision-making.


Popular Beginner Data Analysis Courses and Certifications


  • Status: Free Trial
    Free Trial
    I

    IBM

    Data Analysis with Python

    Skills you'll gain: Exploratory Data Analysis, Data Analysis, Data Import/Export, Data Manipulation, Data Transformation, Predictive Modeling, Data Cleansing, Data Preprocessing, Model Evaluation, Predictive Analytics, Pandas (Python Package), Regression Analysis, Feature Engineering, Statistical Analysis, Matplotlib, Scikit Learn (Machine Learning Library), Data Visualization, NumPy, Python Programming

    4.7
    Rating, 4.7 out of 5 stars
    ·
    20K reviews

    Intermediate · Course · 1 - 3 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    G

    Google

    Google Data Analysis with Python

    Skills you'll gain: Pandas (Python Package), NumPy, Data Manipulation, Data Preprocessing, Package and Software Management, Data Analysis, Data Transformation, Data Integration, JSON, Object Oriented Programming (OOP), Data Wrangling, Data Science, Python Programming, Computer Programming, Programming Principles, Data Import/Export, Software Design, Mathematical Software, Computational Logic, Data Structures

    4.8
    Rating, 4.8 out of 5 stars
    ·
    95 reviews

    Beginner · Specialization · 3 - 6 Months

  • Status: Free Trial
    Free Trial
    Status: AI skills
    AI skills
    I

    IBM

    IBM Data Analyst

    Skills you'll gain: Data Storytelling, Dashboard, Data Presentation, Plotly, Data Visualization Software, Web Scraping, Data Visualization, Interactive Data Visualization, Exploratory Data Analysis, Generative AI, SQL, Data Analysis, Data Manipulation, Statistical Visualization, IBM Cognos Analytics, Excel Formulas, Professional Networking, Data Import/Export, Microsoft Excel, Python Programming

    Build toward a degree

    4.6
    Rating, 4.6 out of 5 stars
    ·
    98K reviews

    Beginner · Professional Certificate · 3 - 6 Months

  • Status: New
    New
    Status: Free Trial
    Free Trial
    G

    Google

    Introduction to Data Analysis Using Python

    Skills you'll gain: Object Oriented Programming (OOP), Data Structures, Data Cleansing, Python Programming, Data Analysis, NumPy, Pandas (Python Package), Data Manipulation, Analytical Skills, Scripting, Algorithms, Debugging

    Beginner · Course · 1 - 4 Weeks

  • Status: Free Trial
    Free Trial
    I

    IBM

    Python for Data Science, AI & Development

    Skills you'll gain: Data Import/Export, Programming Principles, Web Scraping, File I/O, Python Programming, Jupyter, Data Structures, Pandas (Python Package), Data Manipulation, JSON, Computer Programming, Restful API, NumPy, Object Oriented Programming (OOP), Application Programming Interface (API), Automation, Data Analysis

    4.6
    Rating, 4.6 out of 5 stars
    ·
    43K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    M

    Microsoft

    Data Analysis and Visualization with Python

    Skills you'll gain: Generative AI, Matplotlib, Plotly, Data Ethics, Data Visualization, Data Cleansing, Data Manipulation, Exploratory Data Analysis, Pandas (Python Package), Data Transformation, Interactive Data Visualization, Data Visualization Software, Data Storytelling, Data Analysis, Scikit Learn (Machine Learning Library), Jupyter, Python Programming, Machine Learning, Model Evaluation

    4.4
    Rating, 4.4 out of 5 stars
    ·
    102 reviews

    Beginner · Course · 1 - 3 Months

What brings you to Coursera today?

  • Status: Free Trial
    Free Trial
    I

    IBM

    Databases and SQL for Data Science with Python

    Skills you'll gain: SQL, Relational Databases, Stored Procedure, Databases, Query Languages, Jupyter, Data Manipulation, Data Analysis, Pandas (Python Package), Transaction Processing, Python Programming

    4.7
    Rating, 4.7 out of 5 stars
    ·
    23K reviews

    Beginner · Course · 1 - 3 Months

  • Status: Free Trial
    Free Trial
    P

    Packt

    Data Analysis with Pandas and Python

    Skills you'll gain: Pandas (Python Package), Data Import/Export, Matplotlib, Pivot Tables And Charts, Jupyter, Data Visualization Software, Data Visualization, Microsoft Excel, Data Analysis, Text Mining, Data Manipulation, Data Cleansing, NumPy, Data Analysis Software, Data Wrangling, Time Series Analysis and Forecasting, Python Programming, Data Preprocessing, Data Structures, Development Environment

    4.7
    Rating, 4.7 out of 5 stars
    ·
    41 reviews

    Intermediate · Specialization · 3 - 6 Months

  • C

    Coursera

    Python for Data Analysis: Pandas & NumPy

    Skills you'll gain: Pandas (Python Package), NumPy, Data Analysis, Data Science, Python Programming, Data Structures, Exploratory Data Analysis, Data Manipulation, Computer Programming

    4.5
    Rating, 4.5 out of 5 stars
    ·
    381 reviews

    Beginner · Guided Project · Less Than 2 Hours

  • Status: Free Trial
    Free Trial
    U

    University of Pennsylvania

    Data Analysis Using Python

    Skills you'll gain: Matplotlib, Data Analysis, Pandas (Python Package), Data Visualization, Data Science, Data Cleansing, Pivot Tables And Charts, Data Visualization Software, Data Preprocessing, Data Manipulation, Scatter Plots, NumPy, Exploratory Data Analysis, Jupyter, Data Import/Export, Histogram, Python Programming, Data Structures, Programming Principles

    4.5
    Rating, 4.5 out of 5 stars
    ·
    429 reviews

    Beginner · Course · 1 - 4 Weeks

  • Status: New
    New
    Status: Free Trial
    Free Trial
    E

    EDUCBA

    Data Analysis with NumPy and Pandas

    Skills you'll gain: Pandas (Python Package), Pivot Tables And Charts, Data Manipulation, Data Import/Export, NumPy, Time Series Analysis and Forecasting, Business Reporting, Jupyter, Data Wrangling, Microsoft Excel, Data Transformation, Matplotlib, Data Analysis, Data Cleansing, Data Preprocessing, Analytics, Data Processing, Management Reporting, Business Analytics, Python Programming

    4.4
    Rating, 4.4 out of 5 stars
    ·
    7 reviews

    Beginner · Specialization · 1 - 3 Months

  • Status: Free
    Free
    C

    Coursera

    Perform exploratory data analysis on retail data with Python

    Skills you'll gain: Data-Driven Decision-Making, Data Visualization, Business Analytics, Data Analysis, Data Cleansing, Statistical Analysis, Data Preprocessing, Exploratory Data Analysis, Matplotlib, Customer Analysis, Analytical Skills, Trend Analysis, Pandas (Python Package), Python Programming, Jupyter

    4.6
    Rating, 4.6 out of 5 stars
    ·
    21 reviews

    Intermediate · Guided Project · Less Than 2 Hours

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In summary, here are 10 of our most popular data analysis courses

  • Data Analysis with Python: IBM
  • Google Data Analysis with Python: Google
  • IBM Data Analyst: IBM
  • Introduction to Data Analysis Using Python: Google
  • Python for Data Science, AI & Development: IBM
  • Data Analysis and Visualization with Python: Microsoft
  • Databases and SQL for Data Science with Python: IBM
  • Data Analysis with Pandas and Python: Packt
  • Python for Data Analysis: Pandas & NumPy: Coursera
  • Data Analysis Using Python: University of Pennsylvania

Frequently Asked Questions about Beginner Data Analysis

Beginner data analysis refers to the foundational skills and techniques used to interpret and analyze data. It is important because data-driven decision-making is increasingly vital in various fields, from business to healthcare. Understanding data analysis allows individuals to extract meaningful insights from raw data, enabling them to make informed decisions, identify trends, and solve problems effectively. As organizations continue to rely on data, having a grasp of beginner data analysis can empower individuals to contribute significantly to their teams and enhance their career prospects.‎

With skills in beginner data analysis, you can explore various job opportunities across multiple sectors. Common roles include data analyst, business analyst, marketing analyst, and research assistant. These positions often involve collecting, processing, and analyzing data to support decision-making processes. Additionally, industries such as finance, healthcare, and technology are increasingly seeking professionals who can interpret data effectively, making beginner data analysis a valuable skill set in today's job market.‎

To succeed in beginner data analysis, you should focus on developing several key skills. These include proficiency in spreadsheet software like Excel, basic statistical knowledge, and familiarity with data visualization tools. Additionally, understanding how to use programming languages such as Python or R can be beneficial. Learning how to clean and manipulate data, as well as interpret results, are also crucial skills that will enhance your analytical capabilities and prepare you for real-world applications.‎

There are numerous online courses available for beginner data analysis that cater to various learning styles and preferences. Some recommended options include the Data Analysis and Interpretation Specialization, which covers essential techniques and tools, and the Data Analysis with Python Specialization, which focuses on using Python for data analysis tasks. These courses provide structured learning paths and practical exercises to help you build your skills effectively.‎

Yes. You can start learning beginner data analysis on Coursera for free in two ways:

  1. Preview the first module of many beginner data analysis courses at no cost. This includes video lessons, readings, graded assignments, and Coursera Coach (where available).
  2. Start a 7-day free trial for Specializations or Coursera Plus. This gives you full access to all course content across eligible programs within the timeframe of your trial.

If you want to keep learning, earn a certificate in beginner data analysis, or unlock full course access after the preview or trial, you can upgrade or apply for financial aid.‎

To learn beginner data analysis, start by identifying your learning goals and preferred methods. You can enroll in online courses, watch tutorial videos, or read books on the subject. Hands-on practice is crucial, so work on real datasets to apply what you learn. Additionally, consider joining online communities or forums where you can ask questions and share insights with others. This collaborative approach can enhance your understanding and keep you motivated throughout your learning journey.‎

Typical topics covered in beginner data analysis courses include data collection methods, data cleaning and preparation, basic statistical concepts, data visualization techniques, and the use of analytical tools and software. Courses may also introduce you to programming languages like Python or R, as well as how to interpret and communicate findings effectively. By covering these essential topics, beginner data analysis courses equip you with the foundational knowledge needed to analyze data confidently.‎

For training and upskilling employees in beginner data analysis, consider courses like the Data Analysis and Visualization Foundations Specialization, which provides a comprehensive overview of data analysis techniques and visualization tools. Additionally, the Statistics and Data Analysis with Excel Specialization is ideal for those who prefer to work with Excel. These courses can help employees develop essential skills that enhance their productivity and decision-making capabilities.‎

This FAQ content has been made available for informational purposes only. Learners are advised to conduct additional research to ensure that courses and other credentials pursued meet their personal, professional, and financial goals.

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