Are you looking for the best books on Data Analysis?… If yes, then this article is for you. In this article, you will find the Best Data Analysis Books for Beginners & advanced like Beginner courses, and Practice test courses. So, check these Best Data Analysis Books for Beginners and find the Best Data Analysis Books for Beginners to Advanced according to your need.
In the previous article, I shared the Best Free Data Analysis Courses in 2022, you can go through the list and enjoy reading.
Best Data Analysis Books for Beginners to Advanced to know in 2022
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
this hands-on guide is packed with practical case studies that show you how to solve a broad set of data analysis problems effectively. You’ll learn the latest versions of pandas, NumPy, IPython, and Jupiter in the process.
Written by Wes McKinney, the creator of the Python pandas project, this book is a practical, modern introduction to data science tools in Python. It’s ideal for analysts new to Python and for Python programmers new to data science and scientific computing. Data files and related material are available on GitHub.
- Use the IPython shell and Jupiter notebook for exploratory computing
- Learn basic and advanced features in NumPy (Numerical Python)
- Get started with data analysis tools in the pandas library
- Use flexible tools to load, clean, transform, merge, and reshape data
- Create informative visualizations with matplotlib
- Apply the pandas group by facility to slice, dice, and summarize datasets
- Analyze and manipulate regular and irregular time series data
- Learn how to solve real-world data analysis problems with thorough, detailed examples.
Microsoft Excel Data Analysis and Business Modeling (Office 2021 and Microsoft 365)
Master business modeling and analysis techniques with Microsoft Excel and transform data into bottom-line results. Award-winning educator Wayne Winston’s hands-on, scenario-focused guide helps you use today’s Excel to ask the right questions and get accurate, actionable answers.
More extensively updated than any previous edition, new coverage ranges from one-click data analysis to STOCKHISTORY, dynamic arrays to Power Query, and includes six new chapters. Practice with over 900 problems, many based on real challenges faced by working analysts.
- Quickly transition from Excel basics to sophisticated analytics
- Use recent Power Query enhancements to connect, combine, and transform data sources more effectively
- Use the LAMBDA and LAMBDA helper functions to create Custom Functions without VBA
- Use New Data Types to import data including stock prices, weather, information on geographic areas, universities, movies, and music
- Build more sophisticated and compelling charts
- Use the new XLOOKUP function to revolutionize your lookup formulas
- Master new Dynamic Array formulas that allow you to sort and filter data with formulas and find all UNIQUE entries
- Illuminate insights from geographic and temporal data with 3D Maps
- Improve decision-making with probability, Bayes’ theorem, and Monte Carlo simulation and scenarios
- Use Excel trend curves, multiple regression, and exponential smoothing for predictive analytics
- Use Data Model and Power Pivot to effectively build and use relational data sources inside an Excel workbook
SQL for Data Analysis: Advanced Techniques for Transforming Data into Insights
With the explosion of data, computing power, and cloud data warehouses, SQL has become an even more indispensable tool for the savvy analyst or data scientist. This practical book reveals new and hidden ways to improve your SQL skills, solve problems, and make the most of SQL as part of your workflow.
You’ll learn how to use both common and exotic SQL functions such as joins, window functions, subqueries, and regular expressions in new, innovative ways–as well as how to combine SQL techniques to accomplish your goals faster, with understandable code. If you work with SQL databases, this is a must-have reference.
- Learn the key steps for preparing your data for analysis
- Perform time series analysis using SQL’s date and time manipulations
- Use cohort analysis to investigate how groups change over time
- Use SQL’s powerful functions and operators for text analysis
- Detect outliers in your data and replace them with alternate values
- Establish causality using experiment analysis, also known as A/B testing
Qualitative Data Analysis: A Methods Sourcebook
Qualitative Data Analysis: A Methods Sourcebook is the authoritative text for analyzing and displaying qualitative research data. The Fourth Edition maintains the analytic rigor of previous editions while showcasing a variety of new visual display models for qualitative inquiry.
Graphics are added to the now-classic matrix and network illustrations of the original co-authors. Five chapters have been substantially revised, and the appendix’s annotated bibliography includes new titles in research methods. Graduate students and established scholars from all disciplines will find this resource an innovative compendium of ideas for the representation and presentation of qualitative data.
As the authors demonstrate, when researchers “think display,” their analyses of social life capture the complex and vivid processes of the people and institutions studied.
Python Data Analysis: Perform data collection, data processing, wrangling, visualization, and model building using Python
Data analysis enables you to generate value from small and big data by discovering new patterns and trends, and Python is one of the most popular tools for analyzing a wide variety of data. With this book, you’ll get up and running using Python for data analysis by exploring the different phases and methodologies used in data analysis and learning how to use modern libraries from the Python ecosystem to create efficient data pipelines.
Starting with the essential statistical and data analysis fundamentals using Python, you’ll perform complex data analysis and modeling, data manipulation, data cleaning, and data visualization using easy-to-follow examples. You’ll then understand how to conduct time series analysis and signal processing using ARMA models.
As you advance, you’ll get to grips with smart processing and data analytics using machine learning algorithms such as regression, classification, Principal Component Analysis (PCA), and clustering.
In the concluding chapters, you’ll work on real-world examples to analyze textual and image data using natural language processing (NLP) and image analytics techniques, respectively. Finally, the book will demonstrate parallel computing using Dask.
By the end of this data analysis book, you’ll be equipped with the skills you need to prepare data for analysis and create meaningful data visualizations for forecasting values from data.
- Explore data science and its various process models
- Perform data manipulation using NumPy and pandas for aggregating, cleaning, and handling missing values
- Create interactive visualizations using Matplotlib, Seaborn, and Bokeh
- Retrieve, process, and store data in a wide range of formats
- Understand data preprocessing and feature engineering using pandas and scikit-learn
- Perform time series analysis and signal processing using sunspot cycle data
- Analyze textual data and image data to perform advanced analysis
- Get up to speed with parallel computing using Dask
R Cookbook: Proven Recipes for Data Analysis, Statistics, and Graphics
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. The R language provides everything you need to do statistical work, but its structure can be difficult to master. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.
Each recipe addresses a specific problem and includes a discussion that explains the solution and provides insight into how it works. If you’re a beginner, R Cookbook will help get you started. If you’re an intermediate user, this book will jog your memory and expand your horizons. You’ll get the job done faster and learn more about R in the process.
- Create vectors, handle variables, and perform basic functions
- Simplify data input and output
- Tackle data structures such as matrices, lists, factors, and data frames
- Work with probability, probability distributions, and random variables
- Calculate statistics and confidence intervals and perform statistical tests
- Create a variety of graphic displays
- Build statistical models with linear regressions and analysis of variance (ANOVA)
- Explore advanced statistical techniques, such as finding clusters in your data
Head First Data Analysis: A learner’s guide to big numbers, statistics, and good decisions
Today, interpreting data is a critical decision-making factor for businesses and organizations. If your job requires you to manage and analyze all kinds of data, turn to Head First Data Analysis, where you’ll quickly learn how to collect and organize data, sort the distractions from the truth, find meaningful patterns, draw conclusions, predict the future, and present your findings to others.
Whether you’re a product developer researching the market viability of a new product or service, a marketing manager gauging or predicting the effectiveness of a campaign, a salesperson who needs data to support product presentations, or a lone entrepreneur responsible for all of these data-intensive functions and more, the unique approach in Head First Data Analysis is by far the most efficient way to learn what you need to know to convert raw data into a vital business tool.
- Determine which data sources to use for collecting information
- Assess data quality and distinguish signal from noise
- Build basic data models to illuminate patterns, and assimilate new information into the models
- Cope with ambiguous information
- Design experiments to test hypotheses and draw conclusions
- Use segmentation to organize your data within discrete market groups
- Visualize data distributions to reveal new relationships and persuade others
- Predict the future with sampling and probability models
- Clean your data to make it useful
- Communicate the results of your analysis to your audience
Excel Data Analysis For Dummies
Excel is the world’s leading spreadsheet application. It’s a key module in Microsoft Office—the number-one productivity suite—and it is the number-one business intelligence tool. An Excel dashboard report is a visual presentation of critical data and uses gauges, maps, charts, sliders, and other graphical elements to present complex data in an easy-to-understand format.
Excel Data Analysis For Dummies explains in depth how to use Excel as a tool for analyzing big data sets. In no time, you’ll discover how to mine and analyze critical data in order to make more informed business decisions.
- Work with external databases, PivotTables, and Pivot Charts
- Use Excel for statistical and financial functions and data sharing
- Get familiar with Solver
- Use the Small Business Finance Manager
If you’re familiar with Excel but lack a background in the technical aspects of data analysis, this user-friendly book makes it easy to start putting it to use for you.
Murach’s Python for Data Analysis
Data analysts are in demand everywhere today! And now, Murach’s Python for Data Analysis shows you how to do data analysis the way the pros do. You’ll master descriptive analysis, using Pandas to analyze the data and Seaborn to create the visualizations that let you present your findings effectively.
You’ll get started with predictive analysis, using Scikit-learn with linear regression models. And you’ll be guided right from the start by 4 real-world case studies in political, environmental, social, and sports analytics…essential for learning and great perspective for applying your new skills in your own field. See for yourself how quickly and easily this book can turn you into the data analyst that employers are looking for.
Data Analysis Using SQL and Excel
Data Analysis Using SQL and Excel, shows you how to leverage the two most popular tools for data query and analysis—SQL and Excel—to perform sophisticated data analysis without the need for complex and expensive data mining tools. Written by a leading expert on business data mining, this book shows you how to extract useful business information from relational databases.
You’ll learn the fundamental techniques before moving into the “where” and “why” of each analysis, and then learn how to design and perform these analyses using SQL and Excel. Examples include SQL and Excel code, and the appendix shows how non-standard constructs are implemented in other major databases, including Oracle and IBM DB2/UDB.
The companion website includes datasets and Excel spreadsheets, and the book provides hints, warnings, and technical asides to help you every step of the way.
Data Analysis Using SQL and Excel, shows you how to perform a wide range of sophisticated analyses using these simple tools, sparing you the significant expense of proprietary data mining tools like SAS.
- Understand core analytic techniques that work with SQL and Excel
- Ensure your analytic approach gets you the results you need
- Design and perform your analysis using SQL and Excel
Data Analysis Using SQL and Excel, shows you how to best use the tools you already know to achieve expert results.
SQL for Data Analytics: Perform fast and efficient data analysis with the power of SQL
Understanding and finding patterns in data has become one of the most important ways to improve business decisions. If you know the basics of SQL, but don’t know how to use it to gain the most effective business insights from data, this book is for you.
SQL for Data Analytics helps you build the skills to move beyond basic SQL and instead learn to spot patterns and explain the logic hidden in data. You’ll discover how to explore and understand data by identifying trends and unlocking deeper insights.
You’ll also gain experience working with different types of data in SQL, including time-series, geospatial, and text data. Finally, you’ll learn how to increase your productivity with the help of profiling and automation.
By the end of this book, you’ll be able to use SQL in everyday business scenarios efficiently and look at data with the critical eye of an analytics professional.
- Perform advanced statistical calculations using the WINDOW function
- Use SQL queries and subqueries to prepare data for analysis
- Import and export data using a text file and psql
- Apply special SQL clauses and functions to generate descriptive statistics
- Analyze special data types in SQL, including geospatial data and time data
- Optimize queries to improve their performance for faster results
- Debug queries that won’t run
- Use SQL to summarize and identify patterns in data
And here the list ends. So, these are the Best Data Analysis Books for Beginners to Advanced. I will keep adding more Best Data Analysis Books for Beginners to advance to this list.
Conclusion
I hope these Best Data Analysis Books for Beginners to Advanced will definitely help you to enhance your skills. If you have any doubts or questions, feel free to ask me in the comment section.