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Quantaledge Tech Empowerment Foundation

Beginner

Data Analysis

Collecting, cleaning, interrogating and presenting data using Excel, SQL, Python, Power BI and Tableau. Follows the full analysis workflow rather than a tool list, starting at a messy real dataset and finishing with a dashboard and a recommendation a manager can act on.

  • 58 lessons
  • Beginner

Most organizations already hold more data than they use. This course turns a learner into someone who can collect it, clean it, interrogate it and say what it means, using the tools the job actually asks for.

It follows the full analysis workflow rather than a tool list. A learner starts with a messy real dataset and finishes with a dashboard and a recommendation a manager can act on, which is the part most training leaves out.

What a graduate can do

  • Describe the data analysis workflow and tell structured, semi-structured and unstructured data apart.
  • Clean, reshape and summarize data in Excel using advanced formulas, PivotTables and Power Query, and build an interactive dashboard in it.
  • Query a relational database in SQL, including joins, grouping, aggregation, subqueries and window functions.
  • Apply descriptive statistics, sampling, hypothesis testing, confidence intervals, correlation and regression, and read the result of an A/B test correctly.
  • Manipulate and analyze data in Python with NumPy and pandas, and handle CSV, JSON and Excel sources.
  • Choose the right chart for the question and build it in Matplotlib, Seaborn or Plotly.
  • Model data and build published reports in Power BI, writing DAX where the measure calls for it.
  • Build worksheets, calculated fields, dashboards and stories in Tableau and share them.
  • Collect data from APIs and by web scraping, then assess and validate its quality.
  • Run exploratory analysis, cohort analysis, customer segmentation, time series analysis and basic forecasting against business KPIs.
  • Turn a finding into a written recommendation and present it to people who did not do the analysis.

How it is taught

Twelve weeks, two sessions a week of two to three hours, online and instructor-led, with a self-paced track alongside it. Learners work in Excel, a live SQL database, Python in Jupyter notebooks, Power BI Desktop and Tableau Desktop, against real datasets drawn from several industries rather than sample files that have already been cleaned.

Assessment and certification

Weekly assignments and practical exercises, then a capstone analysis presented at the end. The capstone doubles as the first item in a learner's portfolio.

Finishing the course does not produce a certificate on its own. The certificate is issued by Quantaledge Tech Empowerment Foundation and carries the signature of the Chairman.

Before you start

None. No prior programming experience is assumed.

What the course covers

12 modules · 58 lessons

  1. 1. Introduction to data analysis

    What data analysis is and why it matters, structured against unstructured and semi-structured data, the analysis process and workflow, and the career opportunities in the field.

    • What data analysis is and why it matters

      Reading · optional

      Enroll to open
    • Types of data: structured, unstructured and semi-structured

      Reading · optional

      Enroll to open
    • The data analysis process and workflow

      Reading · optional

      Enroll to open
    • Career opportunities in data analysis

      Reading · optional

      Enroll to open

  2. 2. Excel for data analysis

    Advanced functions including VLOOKUP and INDEX-MATCH, PivotTables and PivotCharts, cleaning and transformation, Power Query, and interactive dashboards.

    • Advanced functions and formulas, including VLOOKUP, INDEX-MATCH and IF statements

      Reading · optional

      Enroll to open
    • PivotTables and PivotCharts for summarization

      Reading · optional

      Enroll to open
    • Data cleaning and transformation techniques

      Reading · optional

      Enroll to open
    • Power Query for data import and transformation

      Reading · optional

      Enroll to open
    • Building interactive dashboards in Excel

      Reading · optional

      Enroll to open

  3. 3. SQL for data analysis

    Relational database management systems, SELECT, WHERE, JOIN, GROUP BY and HAVING, aggregation and subqueries, INSERT, UPDATE and DELETE, and window functions.

    • Databases and relational database management systems

      Reading · optional

      Enroll to open
    • Writing queries: SELECT, WHERE, JOIN, GROUP BY, HAVING

      Reading · optional

      Enroll to open
    • Aggregation functions and subqueries

      Reading · optional

      Enroll to open
    • Data manipulation: INSERT, UPDATE, DELETE

      Reading · optional

      Enroll to open
    • Window functions and advanced techniques

      Reading · optional

      Enroll to open

  4. 4. Statistics for data analysis

    Descriptive statistics, probability distributions and sampling, hypothesis testing and confidence intervals, correlation and regression, and statistical significance and A/B testing.

    • Descriptive statistics: mean, median, mode, standard deviation

      Reading · optional

      Enroll to open
    • Probability distributions and sampling

      Reading · optional

      Enroll to open
    • Hypothesis testing and confidence intervals

      Reading · optional

      Enroll to open
    • Correlation and regression analysis

      Reading · optional

      Enroll to open
    • Statistical significance and A/B testing

      Reading · optional

      Enroll to open

  5. 5. Python for data analysis

    Python fundamentals, NumPy for numerical computing, pandas for manipulation and analysis, cleaning and preprocessing, and working with CSV, JSON and Excel.

    • Python fundamentals: variables, data types and control structures

      Reading · optional

      Enroll to open
    • NumPy for numerical computing

      Reading · optional

      Enroll to open
    • pandas for data manipulation and analysis

      Reading · optional

      Enroll to open
    • Cleaning and preprocessing in Python

      Reading · optional

      Enroll to open
    • Working with CSV, JSON and Excel formats

      Reading · optional

      Enroll to open

  6. 6. Data visualization

    Principles of effective visualization, choosing the right chart type, Matplotlib and Seaborn, interactive charts with Plotly, and dashboard design.

    • Principles of effective visualization

      Reading · optional

      Enroll to open
    • Choosing the right chart type

      Reading · optional

      Enroll to open
    • Matplotlib and Seaborn

      Reading · optional

      Enroll to open
    • Interactive visualization with Plotly

      Reading · optional

      Enroll to open
    • Dashboard design

      Reading · optional

      Enroll to open

  7. 7. Power BI

    Power BI Desktop, data import and the Power Query Editor, data modeling and relationships, DAX fundamentals, interactive reports, and publishing through the Power BI Service.

    • Power BI Desktop

      Reading · optional

      Enroll to open
    • Data import and the Power Query Editor

      Reading · optional

      Enroll to open
    • Data modeling and relationships

      Reading · optional

      Enroll to open
    • DAX fundamentals

      Reading · optional

      Enroll to open
    • Interactive reports and dashboards

      Reading · optional

      Enroll to open
    • Publishing and sharing through the Power BI Service

      Reading · optional

      Enroll to open

  8. 8. Tableau

    Tableau Desktop, connecting to and preparing data sources, worksheets and visualizations, calculated fields and table calculations, dashboards and stories, and sharing through Tableau Server.

    • Tableau Desktop

      Reading · optional

      Enroll to open
    • Connecting to data sources and preparing data

      Reading · optional

      Enroll to open
    • Building worksheets and visualizations

      Reading · optional

      Enroll to open
    • Calculated fields and table calculations

      Reading · optional

      Enroll to open
    • Interactive dashboards and stories

      Reading · optional

      Enroll to open
    • Tableau Server and sharing

      Reading · optional

      Enroll to open

  9. 9. Data collection and web scraping

    Collection methodologies, APIs and data extraction, scraping in Python with BeautifulSoup and Scrapy, and data quality assessment and validation.

    • Data collection methodologies

      Reading · optional

      Enroll to open
    • APIs and data extraction

      Reading · optional

      Enroll to open
    • Web scraping in Python with BeautifulSoup and Scrapy

      Reading · optional

      Enroll to open
    • Data quality assessment and validation

      Reading · optional

      Enroll to open

  10. 10. Business intelligence and analytics

    Business metrics and KPIs, exploratory data analysis, cohort analysis and customer segmentation, time series analysis and forecasting, and predictive analytics fundamentals.

    • Business metrics and KPIs

      Reading · optional

      Enroll to open
    • Exploratory data analysis

      Reading · optional

      Enroll to open
    • Cohort analysis and customer segmentation

      Reading · optional

      Enroll to open
    • Time series analysis and forecasting

      Reading · optional

      Enroll to open
    • Predictive analytics fundamentals

      Reading · optional

      Enroll to open

  11. 11. Data storytelling and communication

    Translating insight into a business recommendation, building a data narrative, presenting to stakeholders and executives, and report writing.

    • Translating insight into a business recommendation

      Reading · optional

      Enroll to open
    • Building a data narrative

      Reading · optional

      Enroll to open
    • Presenting to stakeholders and executives

      Reading · optional

      Enroll to open
    • Report writing and documentation

      Reading · optional

      Enroll to open

  12. 12. Capstone project

    An end-to-end analysis on a real-world dataset, from cleaning and exploration through to interactive dashboards, a presentation, and a portfolio piece to keep.

    • An end-to-end analysis on a real-world dataset

      Reading · optional

      Enroll to open
    • Cleaning, exploration and analysis

      Reading · optional

      Enroll to open
    • Interactive dashboards and visualizations

      Reading · optional

      Enroll to open
    • Presentation, and a portfolio piece to keep

      Reading · optional

      Enroll to open

Past cohorts

Runs of this course that have already been taught, with a facilitator and a group working through it together.

Data Analysis - Quantaledge