Unlock the power of data with our comprehensive course, “Data Analysis A–Z: Master the Complete Data Workflow.” This course is designed for beginners and aspiring data professionals who want to gain hands-on experience across the entire data analysis lifecycle. You’ll start by learning how to ask the right questions and collect meaningful data. Then, you’ll dive into cleaning messy datasets, exploring patterns, and performing statistical analysis to extract valuable insights.
You’ll work with industry-standard tools like Excel, Python, and visualization platforms such as Tableau or Power BI. Whether you’re preparing for a data analyst role or simply want to make smarter business decisions, this course gives you the practical skills to analyze, interpret, and present data with confidence.
No prior experience? No problem. Our step-by-step lessons, real-world projects, and expert guidance will take you from beginner to job-ready—A to Z.
What Will You Learn?
- Understand the data analysis workflow: Learn how to approach data from start to finish, including data cleaning, transformation, and exploration.
- Master data cleaning techniques: Handle missing values, duplicates, inconsistent data, and more using Python libraries.
- Manipulate and transform data: Learn how to sort, filter, and merge data effectively, and perform advanced transformations like Box-Cox and Yeo-Johnson.
- Perform exploratory data analysis (EDA): Visualize and summarize data using graphs, descriptive statistics, and groupings to uncover insights.
- Apply hypothesis testing: Conduct t-tests, ANOVA, chi-square tests, and other statistical tests to validate assumptions and make informed decisions.
- Run multiple linear regression analysis: Build, evaluate, and interpret regression models using real-world datasets.
- Use statistical tools: Understand and apply methods such as correlation analysis and hypothesis testing to interpret data.
- Transform data for better analysis: Learn how to apply normality tests and transformation methods to improve the quality of your models.
- Boost productivity with ChatGPT: Use ChatGPT to assist with writing, debugging, and optimizing Python code for efficient data analysis.
- Access valuable resources and tips: Gain access to coding tips, cheat sheets, and online resources to continue growing your data analysis skills.
Course Content
1.What is Data Analysis
Data analysis is the process of examining, organizing, and interpreting data to extract meaningful insights and support informed decisions. It helps individuals and organizations identify patterns, solve problems, and make predictions based on evidence. The process typically involves data collection, cleaning, exploration, visualization, and interpretation. Understanding what data analysis is and why it’s important sets the foundation for mastering the entire data workflow.
Class1:Complete data analysis work flow
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2.Stage 1 Data Cleaning A – Z
Data Cleaning is the first and one of the most critical stages in the data analysis process. In this stage, you transform raw, messy data into a structured, reliable dataset ready for analysis. Poor-quality data leads to inaccurate results—so mastering cleaning is essential.This module covers the A–Z of Data Cleaning, including identifying and handling missing values, removing duplicates, correcting data types, fixing inconsistent formatting, and spotting outliers. You'll also learn how to standardize data, normalize fields, and ensure accuracy across large datasets.Through hands-on examples and real-world scenarios, you’ll practice using tools like Excel, Python (Pandas), or R to automate cleaning workflows. By the end of this module, you’ll be equipped to take any raw dataset and prepare it for high-quality, trustworthy analysis.
Class1:Loading dataset in your jupyter notebook
00:00Class2:Dealing with missing values
00:00Class3:Dealing with inconsistent values
00:00Class4:Dealing with miss identified data types
00:00Class5:Dealing with duplicated data
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3. Stage 2 Data Manipulation A-Z
Data manipulation is the process of transforming and reshaping data to make it suitable for analysis. In this stage, you'll learn the core techniques used to organize, filter, and modify data effectively. Key skills include:Selecting and Filtering Data: Learn how to extract specific rows, columns, or subsets of data based on conditions using boolean indexing, .loc[], .iloc[], and query methods.Sorting and Grouping: Explore how to sort data by columns and group it for aggregation, such as computing sums, means, and other statistical measures.Merging and Joining Data: Understand how to combine multiple datasets using merge(), concat(), and join() functions, ensuring consistent and meaningful relationships between data.Pivoting and Reshaping: Learn how to reshape data into more insightful formats, such as using pivot_table() or melt() for transforming columns into rows.Applying Functions: Discover how to apply functions across datasets using apply(), map(), and applymap() to perform complex transformations efficiently.
Class1:Learn data sorting and arrangement
00:00Class2:Learn conditional data filtering
00:00Class3:Learn to merge extra variables
00:00Class4: Learn to concatenate extra data
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4. Stage 3 Exploratory Data Analysis A-Z
Exploratory Data Analysis (EDA) is a crucial step in the data analysis process where you gain insights and understand the structure, patterns, and anomalies of your data. In this stage, you’ll learn the complete process of EDA, from initial data inspection to advanced visualizations and statistical techniques.
Class1:Exploring value counts analysis method
00:00Class2:Exploring descriptive statistics analysis method
00:00Class3:Exploring group by analysis method
00:00Class4:Exploring pivot table analysis method
00:00Class5:Exploring crosstabulation analysis method
00:00Class6:Exploring correlation analysis method
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5. Stage 4 Understanding Statistical Data Analysis A-Z
This stage introduces the foundational principles and practical techniques of statistical data analysis, which are essential for drawing reliable conclusions from data. You’ll learn how to apply both descriptive and inferential statistics to understand data behavior, relationships, and variability.
Class1:Various aspects of hypothesis testing
00:00Class2:Understand confidence level, significance level and p value
00:00Class3:Understand complete steps in hypothesis testing
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6. Stage 5 Data Transformation A-Z
This stage covers the essential techniques for transforming raw or cleaned data into formats better suited for analysis and modeling. Data transformation plays a critical role in enhancing data quality, improving algorithm performance, and uncovering deeper insights. You’ll learn how to apply mathematical functions, encode categorical data, normalize scales, and create new features from existing variables.
Class1:Testing normal distribution of numeric variables
00:00Class2:Square root transformation for normal distribution
00:00Class3:Logarithmic transformation for normal distribution
00:00Class4:Box cox transformation for normal distribution
00:00Class5:Yeo Johnson transformation for normal distribution
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7. Stage 6 Hypothesis Testing A-Z
In this stage, you'll gain a comprehensive understanding of hypothesis testing, a fundamental method used to draw conclusions and make decisions based on data. You'll learn how to construct and test assumptions about a population using sample data, enabling you to validate insights with statistical confidence.Key topics covered include:Formulating null and alternative hypothesesUnderstanding p-values, significance levels, and confidence intervalsPerforming various hypothesis tests such as t-tests, chi-square tests, and ANOVAExploring one-tailed vs two-tailed testsIdentifying and avoiding Type I and Type II errorsEnsuring assumptions like normality and equal variances are metInterpreting test results and making data-driven conclusionsThrough hands-on examples and real-world scenarios, you'll learn to apply hypothesis testing techniques effectively, enhancing the statistical rigor of your analyses and guiding better business or research decisions.
Class1:One way between groups ANOVA
00:00Class2:Pearson product moment correlation coefficient
00:00Class3:Multiple linear regression analysis with statsmodel api
00:00
8. Tips, Tricks and Resources!
This final stage is packed with valuable productivity tips, time-saving tricks, and expert resources to help you work smarter as a data analyst. Whether you’re cleaning messy data, building models, or visualizing results, these insights will streamline your workflow and deepen your expertise.You’ll discover:Practical shortcuts in Jupyter Notebook and PandasCommon debugging strategies for faster problem-solvingBest practices for writing clean, reusable codeTime-saving techniques for data exploration and visualizationHandy cheat sheets, documentation hubs, and online toolsCurated learning resources to keep your skills sharp and up-to-dateThis stage also highlights communities, blogs, and platforms where professionals share real-world datasets, code examples, and career advice.By the end, you’ll be equipped with a toolkit of smart techniques and go-to resources that will make your data analysis journey more efficient, enjoyable, and impactful.
Class:1ChatGPT for smooth python coding in Data Analysis
00:00Data Analysis A–Z: Master the Complete Data Workflow
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