Data Analytics Learning Path

Learn Data Analytics: From Raw Data to Business Decisions

A structured data analytics path written by working analysts. Start with the fundamentals and statistics, then build the practical toolkit — pandas, data cleaning, analytical SQL and visualization — that turns messy data into decisions. Every tutorial pairs the concept with a runnable example, the mistakes beginners make, and how the topic shows up in interviews.

  • 70 Tutorials
  • 6 Topic Clusters
  • Beginner
  • Free Learning

Data Analytics Roadmap

Follow the Path in Order

01

Fundamentals 12 tutorials

What data analytics is, the analysis process, and the vocabulary every analyst uses.

beginnerThe Data Analysis Process, Step by StepThe data analysis process turns a business question into an answer through six repeatable steps. Learn each stage with a practical worked example.4 min readbeginnerData Collection Methods for AnalystsData collection methods range from surveys and interviews to sensors and existing records. Learn the main types, primary vs secondary data, and pitfalls.4 min readbeginnerData-Driven Decision Making ExplainedData-driven decision making uses evidence instead of gut feel to guide choices. Learn the process, see an example, and avoid the common traps.4 min readbeginnerData Quality Dimensions Every Analyst NeedsData quality has six dimensions: accuracy, completeness, consistency, timeliness, validity and uniqueness. Learn what each means and how to check it.4 min readbeginnerKPIs and Metrics: What Analysts TrackA metric is any measurement; a KPI is a metric tied to a goal. Learn the difference, see examples, and how analysts pick and calculate the ones that matter.4 min readbeginnerLevels of Measurement in StatisticsThe four levels of measurement — nominal, ordinal, interval and ratio — decide which statistics are valid on your data. Learn each with clear examples.4 min readbeginnerPopulation vs Sample in StatisticsA population is every member of a group; a sample is a subset you actually measure. Learn the difference, why we sample, and how to sample well.4 min readbeginnerQualitative vs Quantitative Data ExplainedQuantitative data is numbers you can measure; qualitative data describes qualities in words or categories. Learn the difference and when each matters.4 min readbeginnerSpreadsheets vs Databases for AnalystsSpreadsheets are flexible and quick; databases are structured and scale to millions of rows. Learn the differences and when analysts should switch.4 min readbeginnerStructured vs Unstructured DataStructured data fits neat rows and columns; unstructured data like text and images does not. Learn the difference, examples, and how analysts handle each.4 min readbeginnerThe Four Types of Data AnalyticsAnalytics comes in four types: descriptive, diagnostic, predictive and prescriptive. Learn what each answers and when to use it, with practical examples.4 min readbeginnerWhat Is Data Analytics? A Beginner's GuideData analytics is the practice of turning raw data into answers. Learn what it means, the four types, and how analysts use it to drive real decisions.4 min read
02

Statistics 12 tutorials

The descriptive and inferential statistics that back every honest analysis.

beginnerThe Central Limit Theorem in Plain EnglishThe central limit theorem says sample means become normally distributed as sample size grows, even when the data is not. Learn why it powers most statistical tests.5 min readbeginnerCorrelation vs Causation for AnalystsCorrelation shows two variables move together; causation means one drives the other. Learn why confounders create false links and how to reason about cause.5 min readbeginnerDescriptive Statistics for Data AnalystsDescriptive statistics summarize a dataset with a handful of numbers for center, spread and shape. Learn what each measure tells you and how to compute them.4 min readbeginnerHypothesis Testing Basics for AnalystsHypothesis testing decides whether data supports a claim beyond chance. Learn the null and alternative hypotheses, significance level, and the decision rule.5 min readbeginnerMean, Median and Mode ExplainedMean, median and mode are the three averages. Learn how each is computed, how outliers pull them apart, and which one to report for skewed data.4 min readbeginnerThe Normal Distribution and Z-ScoresThe normal distribution is the bell curve behind most statistics. Learn the 68-95-99.7 rule, how to compute z-scores, and why the bell shape matters.5 min readbeginnerP-Values Explained Without the JargonA p-value is the probability of data at least this extreme if the null hypothesis were true. Learn what it means, what it does not, and how to read it correctly.5 min readbeginnerPearson vs Spearman CorrelationPearson measures linear association; Spearman measures monotonic association using ranks. Learn how each is computed and when to prefer one over the other.4 min readbeginnerPercentiles and Quartiles Made ClearPercentiles and quartiles describe position in sorted data. Learn to compute them, find the interquartile range, and use the 1.5 IQR rule to detect outliers.5 min readbeginnerProbability Basics for Data AnalysisProbability measures how likely events are. Learn sample spaces, the addition and multiplication rules, independence and conditional probability with clear examples.5 min readbeginnerProbability Distributions ExplainedA probability distribution describes how likely each outcome of a variable is. Learn discrete versus continuous distributions and the common families analysts use.5 min readbeginnerVariance and Standard DeviationVariance and standard deviation measure how spread out data is. Learn the formulas, the population versus sample difference, and how to compute both correctly.5 min read
03

Pandas & NumPy 12 tutorials

The Python analyst toolkit — load, filter, group and reshape real datasets.

beginnerNumPy Tutorial: Arrays for Data AnalysisNumPy is the array library beneath pandas. Learn to create arrays, do vectorized math, index and slice, and compute aggregations, with runnable examples.4 min readbeginnerPandas apply, map and applymapapply and map run your own functions over pandas data. Learn Series.map, DataFrame.apply on columns and rows, and why vectorized code is usually faster.4 min readbeginnerPandas Datetime Handling for AnalysisDates power most analysis. Learn to parse them with to_datetime, extract parts via the dt accessor, filter by date range, and resample time series in pandas.4 min readbeginnerPandas: Filtering Rows by ConditionFiltering rows by condition is the most common pandas operation. Learn boolean masks, combining conditions, isin, between and query with runnable examples.4 min readbeginnerPandas GroupBy: Aggregate and SummarizeGroupBy turns detailed rows into category summaries. Learn split-apply-combine, common aggregations, agg with multiple functions, and grouping by many columns.4 min readbeginnerPandas Tutorial: A Beginner's IntroductionPandas is the core Python library for data analysis. Learn what it is, how to build a DataFrame, and how to run your first real inspection on tabular data.5 min readbeginnerPandas loc vs iloc: Selecting Dataloc and iloc are how you precisely select rows and columns in pandas. Learn the difference between label-based and position-based indexing with examples.4 min readbeginnerPandas Merge and Join ExplainedMerge combines two DataFrames on a common key. Learn inner, left, right and outer joins, how to pick join keys, and how merge differs from concat.4 min readbeginnerPandas Pivot Tables for AnalysisPivot tables reshape data into cross-tabs the way spreadsheets do. Learn pivot_table with index, columns, values and aggfunc, plus totals and groupby differences.4 min readbeginnerPandas: Read CSV and Excel FilesReal analysis starts by loading a file. Learn pandas read_csv and read_excel, including headers, delimiters, missing values, and selecting columns and sheets.4 min readbeginnerPandas DataFrame and Series ExplainedThe Series and DataFrame are the two objects the whole pandas library is built on. Learn how they relate, how to create them, and how to move between them.4 min readbeginnerPandas String Methods for Cleaning TextText columns are messy. Learn the pandas str accessor: lower, strip, contains, replace, split and extract to clean and parse strings across a whole column.4 min read
04

Data Cleaning 12 tutorials

Turn messy real-world data into something you can trust — the analyst's daily work.

beginnerCategorical Data Encoding BasicsModels need numbers, not text categories. Learn one-hot and label encoding in pandas, when each fits, and how to avoid the ordering trap, with examples.5 min readbeginnerData Validation Rules for AnalystsValidation rules catch bad data before it reaches a report. Learn range, type, uniqueness and allowed-value checks in pandas, and how to flag failing rows.4 min readbeginnerFixing Data Types in PandasNumbers stored as text and dates stored as strings block real analysis. Learn to fix data types in pandas with astype, to_numeric and to_datetime.5 min readbeginnerFixing Date Formats in PandasDates arrive in a dozen formats and as plain text. Learn to parse them into real datetimes with pandas to_datetime, handle day-first order, and extract parts.5 min readbeginnerHandling Missing Values in PandasMissing values break sums, averages and models. Learn to detect them, decide whether to drop or fill, and apply dropna and fillna correctly in pandas.4 min readbeginnerImputation Techniques for Missing DataImputation fills gaps instead of deleting rows. Learn mean, median, mode, forward-fill and group-based imputation in pandas, and when each one is the right call.4 min readbeginnerFixing Inconsistent Category Labels'NY', 'New York' and 'new york' should be one category. Learn to find label variants and map them to a single clean value in pandas so groupings are correct.5 min readbeginnerNormalization vs StandardizationBoth rescale numeric features but differently. Learn normalization (min-max, 0 to 1) versus standardization (z-score, mean 0), when each fits, with pandas code.4 min readbeginnerOutlier Detection and TreatmentOutliers can be typos or real extremes. Learn to detect them with the IQR and z-score methods in pandas, and decide whether to keep, cap or remove each one.5 min readbeginnerRemoving Duplicate Records in PandasDuplicate rows silently inflate every count and total. Learn to detect them with duplicated() and remove them with drop_duplicates(), including subset and keep.4 min readbeginnerStandardizing Text Data in PandasExtra spaces and mixed casing split one value into many. Learn to standardize text columns in pandas with strip, lower, title and replace so categories align.5 min readbeginnerWhat Is Data Cleaning in AnalyticsData cleaning turns raw, messy data into a reliable table you can analyze. Learn what it covers, why it matters, and see a first pandas cleanup end to end.5 min read
05

SQL for Analytics 11 tutorials

The SQL an analyst actually writes to pull and shape data for reports.

beginnerSQL Aggregate Functions for AnalystsTurn thousands of rows into one number: total revenue, average order value, customer counts. Learn the five aggregate functions analysts use in every report.4 min readbeginnerSQL CASE Statements for Data AnalystsThe analyst's if-then tool. Learn the SQL CASE statement to label rows, bucket values into tiers, and pivot data with conditional aggregation.4 min readbeginnerSQL CTEs for Data AnalystsComplex reports read better as named steps. Learn the CTE (WITH clause) to structure multi-stage analytics queries clearly and reuse intermediate results.4 min readbeginnerSQL for Data Analysts: A Practical StartThe 20% of SQL that answers 80% of business questions. Learn the analyst's core toolkit for turning raw tables into sales, customer and revenue reports.5 min readbeginnerGROUP BY and HAVING for ReportingBreak totals down by region, month or product, then keep only the groups that matter. Learn GROUP BY and HAVING as analysts use them for segmented reports.4 min readbeginnerSQL Joins Explained for Data AnalystsBusinesses split data across tables. Learn INNER, LEFT, RIGHT and FULL joins to stitch orders to customers and products for reports that show names, not IDs.4 min readbeginnerORDER BY and LIMIT for ReportingTop 10 customers, biggest orders, most recent sales. Learn ORDER BY and LIMIT to rank and trim result sets the way analysts build ranked reports.4 min readbeginnerSELECT and WHERE for Data AnalystsEvery report starts by choosing columns and narrowing rows. Master SELECT and WHERE to filter sales, orders and customer data the way analysts do daily.4 min readbeginnerSQL Self Joins for Data AnalystsSome questions need a table joined to itself: employees and their managers, or comparing one row to another. Learn self joins with clear analyst examples.4 min readbeginnerSQL Subqueries for Data AnalystsWhen one business question depends on the answer to another, you nest queries. Learn scalar, IN and correlated subqueries with practical analyst examples.4 min readbeginnerSQL Window Functions for AnalystsRank within groups, compute running totals, compare each row to its group average. Learn window functions, the skill that separates strong analysts from beginners.4 min read
06

Data Visualization 11 tutorials

Choose the right chart and tell a clear, honest story with data.

beginnerBar Charts Best PracticesBar charts are the workhorse of comparison. Learn the rules that make them clear: zero baseline, sorting, direct labels, and when to go horizontal or grouped.4 min readbeginnerBox Plots Explained for AnalystsA box plot packs a distribution into five numbers and makes comparing groups effortless. Learn to read the median, quartiles, whiskers, and outliers.4 min readbeginnerChoosing the Right Chart TypePick a chart by the question you are answering. A simple decision framework mapping comparison, trend, distribution, relationship and composition to the right chart type.4 min readbeginnerColor in Data VisualizationColor is a data channel, not decoration. Learn to match sequential, diverging and categorical palettes to your data, stay colorblind-safe, and highlight with restraint.4 min readbeginnerData Visualization PrinciplesThe core principles that separate a chart that informs from one that confuses: honest encoding, clarity, and matching the mark to the question you are answering.5 min readbeginnerHeatmaps in Data AnalysisHeatmaps turn a grid of numbers into a field of color, making patterns across two dimensions pop. Learn correlation matrices, cross-tabs, and honest color scales.4 min readbeginnerHistograms Explained for AnalystsA histogram reveals the shape of a single variable: where values cluster, how they spread, and where outliers sit. Learn bins, skew, and the bin-width trap.4 min readbeginnerLine Charts for Time SeriesLine charts are built for time. Learn to show trends clearly, handle multiple series without spaghetti, choose the right time granularity, and label directly.4 min readbeginnerMisleading Charts and How to Spot ThemCharts can lie without a single false number. Learn the common distortions — truncated axes, dual scales, cherry-picked ranges — and how to spot and avoid them.4 min readbeginnerPie Charts: When to Use ThemPie charts are widely overused. Learn the narrow cases where they work, why bar charts usually beat them, and the rules for a pie that actually communicates.4 min readbeginnerScatter Plots: A Practical GuideScatter plots show how two variables move together. Learn to read correlation, spot clusters and outliers, add a trend line, and handle overplotting in dense data.4 min read

Continue Your Preparation

Data Analytics Interview PreparationTopic-wise and experience-level question sets with answers.Developer Roadmaps & Career GuidesSkills, timelines and milestones for the roles you are targeting.Technology ComparisonsHonest side-by-side guides to help you choose what to learn.Java Full Stack ProgramCodeBegun's 145-day project-based program in Madhapur, Hyderabad.
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