Data Analyst: SQL, Analysis, and Visualization

Strengthen your core data analysis skills by learning how to summarize, profile, and explore datasets in ways that support real business questions. In this course, you’ll build practical experience used in roles such as data analyst, business analyst, reporting analyst, operations analyst, and marketing analyst. You’ll work with common analysis techniques to examine distributions, summarize categorical and numeric data, compare variables, and support segmentation through filtering and exploratory visuals. This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from profiling datasets and building summary tables to exploring variable relationships and helping stakeholders investigate data by segment. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data analysis experience. By the end, you’ll be able to use aggregation, cross-tabulation, frequency analysis, and measures of central tendency and dispersion to summarize data, apply charts and graphical methods to understand distributions and relationships, and use filtering and correlation techniques to support segmentation and exploratory analysis. This course is a strong fit if you already have basic experience with spreadsheets, data analysis, or working with tables and charts.

In Data Cleaning, Transformation, and Manipulation, you’ll learn to turn messy data into analysis- and modeling-ready datasets using Python (pandas) and SQL. This is a skill-based path organized around real workplace tasks. Each module mirrors responsibilities you see in job descriptions and focuses on the exact steps you’ll perform on the job. You’ll begin with a quick skills check, then personalize your journey: double down on new topics, or skip what you already know. For each skill, you’ll review concise lessons curated from expert instructors with explanations and demos for filtering and subsetting, joins and merges, feature engineering, normalization, encoding, imputation, scaling, and feature selection. Then you will prove your skills in job-task assessments. By the end, you can assemble analysis-ready tables, engineer clean numeric features, and prepare a modeling-ready feature set for predictive modeling. These capabilities support roles like Data Analyst, Analytics Engineer, Business Intelligence Analyst, Data Scientist, or Machine Learning Engineer and help you handle everyday tasks such as combining datasets, cleaning and transforming columns, and delivering ready-to-train features.

Build practical data quality and debugging skills that help you investigate issues, clean datasets, and validate reporting outputs with confidence. In this course, you’ll develop hands-on experience used in roles such as data analyst, reporting analyst, business analyst, operations analyst, and data quality analyst. You’ll practice profiling datasets, summarizing data characteristics with queries and programming techniques, identifying common quality issues, and tracing likely sources of problems that can affect reporting and analysis. This is a non-traditional, skill-based learning experience organized around real workplace tasks instead of a fixed lecture sequence. It’s designed to reflect responsibilities you may see in job descriptions, from handling missing values and removing duplicate records to validating report outputs and debugging SQL logic when data discrepancies appear. You can personalize your path based on what you already know, focus on the skills you need most, and skip content when it’s not necessary. The course curates high-quality lessons from expert instructors, selecting the strongest content for each skill so you can build practical, career-relevant data quality experience. By the end, you’ll be able to profile datasets using basic queries and programming techniques, identify likely data quality issues through routine checks, address missing values and duplicate records, perform validation checks on report data, and debug SQL queries to resolve logical errors and reporting discrepancies. This course is a strong fit if you already have basic experience with datasets, spreadsheets, SQL, or reporting and analysis workflows.

In Data Visualization and Reporting, you’ll learn to turn raw data into clear charts, dashboards, and reports that people actually use. This course is organized around workplace skills and job tasks, mirroring responsibilities you’ll see in real job descriptions. Start by checking what you already know, then focus on the areas you want to strengthen. If you’re confident, skip ahead. If a topic is new, review targeted lessons curated from multiple expert instructors. You’ll practice choosing the right chart for an analytical purpose, applying visual design for clarity and impact, and building visuals in Tableau and Excel. Then you’ll assemble them into readable dashboards and reports, format layouts for consistency, and add titles and annotations that surface the “so what.” Each module culminates in a graded, job-task assessment to help you validate progress. By the end, you’ll be ready to perform common tasks in roles like Data Analyst, Business Intelligence Analyst, Reporting Analyst, Operations Analyst, or Dashboard Developer.

In Descriptive Statistics and Data Visualization, you’ll learn to explore datasets with code and turn findings into targeted visuals that drive clear decisions. This is a skill-based path organized around real analyst job tasks. You’ll start with a quick check of what you already know, then focus on the skills you want to strengthen. Skip topics you’ve mastered and dive deeper where you need practice. Each lesson is curated from expert instructors so every step builds a concrete workplace skill. Using Python (pandas, seaborn/matplotlib), you’ll perform exploratory data analysis (EDA), compute and interpret measures of central tendency and dispersion, summarize categorical variables with frequency analysis, and create exploratory charts. In Tableau, you’ll identify the right chart for a question and build comparison visuals that stack KPIs against targets and across business segments. By the end, you can explore data reproducibly, summarize results clearly, and design targeted visuals including skills that map to responsibilities in roles like Data Analyst, Business Intelligence Analyst, Reporting Analyst, and Operations Analyst.