Data Analyst: Professional Certificate in Data Analysis

Learn data cleaning and preparation to ensure accurate analysis. Apply missing value imputation, outlier handling, and data transformation using normalization, aggregation, and encoding with Excel, Python, and R.

Explore exploratory data analysis (eda) to uncover patterns, outliers, and trends using descriptive statistics and visualizations such as histograms, box plots, and scatter plots.

Apply inferential statistics to model relationships, test hypotheses, and estimate population parameters using confidence intervals, correlation, regression, ANOVA, and chi-square to guide data-driven decisions.

Turn raw data into clear visuals with dashboards and charts to reveal patterns and trends, using Tableau, Power BI, matplotlib, or ggplot2 to deliver audience-specific, real-time, actionable insights.

Forecast future outcomes using historical data by building predictive models with machine learning techniques, such as linear regression, decision trees, and clustering, and evaluate with accuracy, precision, and recall.

Translate data analysis into meaningful insights and actionable recommendations that inform business decisions for non-technical stakeholders through clear reports and engaging presentations.

Learn how data privacy and ethics shape responsible data analysis under GDPR and CcpA. Apply data minimisation, consent, access rights, encryption, and audits to protect individuals and ensure transparency.

Build a professional data analyst portfolio with diverse projects in cleaning, analysis, visualization, and modeling, using SQL, Python or R, and Tableau, with clear documentation.

Transform raw data into actionable insights by collecting, cleaning, and interpreting data. Leverage predictive analytics, visualization, and data driven decision making to drive business value and competitive advantage.

Explore exploratory data analysis to understand data structure, spot outliers, identify trends, and generate hypotheses using descriptive statistics and visualizations such as histograms, box plots, and heatmaps, guiding subsequent modeling.

Explore how data analysis guides business decisions through Target's predictive modeling case, scoring pregnancy likelihood to tailor offers while navigating privacy and ethics.

Gain hands-on data analysis experience through practical exercises with commonly used tools. Alex, a product researcher and lecturer with a PhD in anthropology, guides this data analyst professional certificate course.

Develop hands-on data cleaning, analysis, and visualization skills across Excel, SQL, Python, R, and Tableau, including missing data, descriptive statistics, and regression.

Learn to clean and analyze data in Excel by identifying missing values, replacing them with the average, using sumifs and pivot tables to summarize regional sales and identify top salesperson.

Practice statistical analysis in R with missing data handling, descriptive statistics, regression, and data visualization in Posit Cloud, including loading data and creating scatter plots.

Create Tableau visualizations of 2024 earthquakes from the USGS catalog, with magnitude-based map size, depth color, time animation, filtering, and a three-map dashboard.

Celebrate completing the course and apply hands-on skills in Excel, SQL, Python, R, and Tableau to future projects; stay tuned for MTF courses.

Explore advanced excel features to boost data analysis, automate workflows, and create dynamic visual reports. Apply the tools and knowledge to real-world work and boost productivity.

Master advanced formulas and functions, data analysis and visualization with pivot tables and dynamic charts, data cleaning, macros and VBA automation, and advanced filtering and dynamic arrays.

Learn to apply if, and, or functions to classify sales performance and verify conditions like above 1000, above 100 in all months, or above 300 in any month.

Learn how lookup and reference functions work, including vlookup, index, and match, to retrieve exact or approximate values from tables, as shown with widget prices.

Learn to create and customize pivot tables to summarize, analyze, and compare sales data with sums, averages, and counts using rows, columns, values, and slicers.

Explore how Excel conditional formatting highlights data by rules, such as high grades and top 10%, using formulas and dynamic color cues to reveal trends and outliers.

Learn to create and manage data tables for dynamic data analysis, using sorting, filtering, and a total row to update charts and formulas with structured references.

Learn to enforce data integrity in Excel with data validation, drop-down lists, and custom rules to prevent invalid entries and display helpful alerts.

Explore text to columns to split data by delimiters into separate fields, such as turning product codes into type id and variant for clean csv data.

Master essential VBA commands for automating Excel tasks, including sub procedures, variables, control structures, loops, ranges and cells, message and input boxes, error handling, functions, objects, and events.

Learn to record macros with VBA in Excel and Word to automate repetitive tasks, save time, ensure accuracy, and save work as a macro-enabled workbook with a practical example.

Automating tasks with Excel macros streamlines workflows, reduces repetitive effort, minimizes errors, freeing time for strategic data analysis and reliable report generation.

Use Get and Transform to import a CSV, rename columns to employee name, team, and annual salary, filter out salaries below 60,000, then close and load to a new worksheet.

Integrate SQLite and Excel to create a robust data workflow that stores and queries data, exports to CSV, analyzes with Excel pivot tables and charts, and enhances collaboration.

Build on your advanced excel skills by mastering complex formulas, data analysis, and automation with VBA. Apply these techniques to real world practice and explore power BI and SQL.

Learn to use SQL Lite and SQLite from basics to advanced, performing data retrieval, manipulation, joins, subqueries, and indexing for real-world analytics.

Learn to retrieve and manipulate data from tables using select, where, order by, and limit, with filter conditions and ascending or descending sorting by price and quantity.

Explore advanced queries using aggregate functions, group by, and having to analyze data by category; calculate total and average prices, count products per category, and filter groups for business insights.

Learn four join types—inner, left, right, and full outer—for combining data, then practice an inner join to link products with categories and query their details.

modify data in sql with update and delete, enforce where clauses, and use transactions: begin, commit, and rollback, to practice price updates, deletions, and category-based discounts on a products table.

Learn how to optimize SQL queries with indexing and explain plans to speed data retrieval on large datasets, including composing indexes and analyzing performance bottlenecks.

Master data-driven marketing decisions by learning foundations, tools, and strategies to optimize campaigns, boost customer insights, and turn data into actionable marketing intelligence.

Explore how marketing data underpins modern strategies by distinguishing quantitative from qualitative insights and structured from unstructured formats. Combine these data types to optimize campaigns and reveal customer needs.

Explore four marketing data analysis frameworks—descriptive, diagnostic, predictive, and prescriptive—showing how to summarize past performance, uncover causes, forecast outcomes, and prescribe actions.

Predictive analytics uses regression, classification, and decision trees to forecast sales, segment customers, optimize campaigns, and predict churn and lead scoring to improve marketing decisions.

Learn to collect, process, and analyze sales and service data using key analytical tools and techniques to improve decision making, customer experience, and performance.

Analyze the sales pipeline from lead generation to post-sale follow-up using funnel analysis, cohort analysis, and predictive modeling to optimize conversion rates and forecast revenue.

Data governance is a framework that sets policies, responsibilities, and procedures to manage data assets, ensuring quality, privacy, and GDPR compliance, guiding decision making through Cobit and ISO 38 500.

Define data governance roles—data owners, stewards, and custodians—and align data management with business goals, establish policies, ensure data quality, and promote cross-functional collaboration under the chief data officer.

Develop data management fundamentals: governance, architecture, modeling, storage, security, and interoperability to ensure accurate data, regulatory compliance, and trusted decision making across systems.

Appoint a chief data officer and establish a data governance office. Define scope, choose a centralized, decentralized, or hybrid model, and implement governance policies across data management.

Discover how data governance tools ensure data quality, metadata management, and compliance, and learn to select and implement tools like Collibra, Informatica, talent, IBM, Erwin, ASG technologies, and SAP.