Data Analytics Certificate
Live data analytics certificate program in SQL, Python, Excel, and Power BI
Learn to query, clean, analyze, and present data with SQL, Python and Pandas, statistics, Excel, and Power BI, then apply machine learning with scikit-learn, in a small live online cohort taught by an experienced data instructor. Build portfolio projects throughout and finish with a Capstone presented live. No coding or math background needed.
- Training careers nationwide since 2006
- 100% live instruction, never pre-recorded
- WIOA and DVR funding eligible
Program snapshot
Schedule
Upcoming Data Analytics Certificate schedule
Each cohort meets live on Zoom three evenings per week on a set schedule. Reserve your seat early: cohorts are kept small, and funding approvals such as WIOA take time.
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Start Date Oct 19, 2026End Date Apr 14, 2027Tuition From $7,995.00ScheduleMon / Wed / Thu, 5:00 to 8:00pm MT (7:00 to 10:00pm ET). No class Nov 23 to 27, Dec 21 to Jan 1, Jan 18, or Feb 15.
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Start Date Jan 11, 2027End Date Jun 30, 2027Tuition From $7,995.00ScheduleMon / Wed / Thu, 5:00 to 8:00pm MT (7:00 to 10:00pm ET). No class Jan 18, Feb 15, Mar 15 to 19, or May 31 to Jun 4.
Neither date works for you? Contact Admissions and we will notify you as soon as the next cohort is scheduled.
Next cohort dates coming soon
Tell us when you would like to begin and we will notify you as soon as that cohort opens.
Request program informationCurriculum
What the data analytics certificate covers in 198 hours
The curriculum moves through the tools data analyst roles actually require, in the order that builds real proficiency. You start with SQL, because every data environment runs on databases and retrieving data cleanly is the first skill any analyst needs. From there you move into Python and Pandas for data manipulation and visualization, statistics and interpretation, advanced SQL, Excel, and Power BI for reporting, and machine learning with scikit-learn, finishing with a Capstone that brings the full stack together in one portfolio-ready project.
SQL Fundamentals and Data Retrieval (18 hrs)
Outcome: query relational databases with confidence, from SELECT and JOIN through aggregates, subqueries, and set operations.
- Introduction to relational databases, tables, and schema design
- SQL syntax basics: SELECT, FROM, WHERE, ORDER BY, and LIMIT
- JOIN operations: INNER JOIN for combining data across tables
- Advanced filtering with complex WHERE clauses and logical operators
- Aggregate functions: SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING
- Subqueries and set operations: UNION, INTERSECT, and EXCEPT
- Introduction to data analytics tools and program overview
- Project 1 kickoff: build toward a presentation to the class
Python Fundamentals (36 hrs)
Outcome: write working Python, from environment setup in Anaconda and Jupyter Notebooks through functions, files, and error handling.
- Python environment setup using Anaconda and Jupyter Notebooks
- Basic syntax, variables, data types, and operators
- Conditional statements: if, elif, and else
- Loops: for and while, including break, continue, and pass
- Data structures: lists, tuples, sets, and dictionaries
- Functions: arguments, return values, scope, and lambda functions
- File handling: reading from and writing to files
- Error and exception handling: try-except blocks and the finally clause
Python Pandas, Data Analysis, and Visualization (36 hrs)
Outcome: clean, explore, and visualize real datasets with Pandas, matplotlib, seaborn, and Plotly.
- DataFrames and Series: reading, writing, and working with data
- Functions, groupby, sorting, and indexing
- Exploratory data analysis (EDA) and data cleaning: missing values, duplicates, and filtering
- Pandas join operations and real-world dataset practice
- Feature engineering for machine learning pre-processing
- Histograms, scatterplots, bar charts, line graphs, and boxplots
- Customizing visualizations with matplotlib, seaborn, Plotly, and other Python libraries
- Applying visualization to real business scenarios
Statistics and Interpretation (18 hrs)
Outcome: apply descriptive and inferential statistics to real data and explain what the results mean for a business decision.
- Introduction to statistics: terminology and foundational concepts
- Descriptive statistics and measures of central tendency
- Inferential statistics and hypothesis testing
- Confidence intervals and p-value interpretation
- Probability, random variables, and probability distributions
- Applying statistical frameworks to real datasets to recognize patterns and support decisions
Advanced SQL, Excel, Power BI, and Reporting (27 hrs)
Outcome: write advanced SQL, analyze and report in Excel, and present findings in interactive Power BI dashboards.
- Complex JOIN operations: LEFT, RIGHT, and FULL OUTER JOINs, and self joins
- Data modification: INSERT, UPDATE, and DELETE
- Indexing and query performance: query planning and optimization
- Pivot and unpivot operations for data transformation and reporting
- Analytical Excel functions for statistical analysis and moving averages
- Advanced Excel data analysis: manipulation, reporting, and optimization for business use
- Building interactive dashboards and reports in Power BI
AI and Machine Learning (45 hrs)
Outcome: build, evaluate, and explain supervised and unsupervised models with scikit-learn, and use AI tools to work faster as an analyst.
- Machine learning within data analysis and data science
- Train-test split and data encoding
- Supervised learning: linear and logistic regression, decision trees, random forests, support vector machines, and Naive Bayes
- Model evaluation: confusion matrix, L1 and L2 regularization, grid search, and cross-validation
- Unsupervised learning: K-means clustering and principal component analysis (PCA)
- Ensemble methods: bagging
- Applying scikit-learn to real-world classification, regression, and clustering problems
- Using AI tools to improve analytical productivity
Capstone Project and Portfolio (18 hrs)
Outcome: bring the full stack together in one portfolio-ready analysis, present it live, and prepare for the analyst job search.
- Final Capstone integrating SQL, Python, Pandas, Excel, Power BI, and machine learning into a single portfolio-ready deliverable
- Live presentation to the class with structured peer and instructor critique
- Portfolio review, design, and refinement
- Job search strategies and current labor market guidance for analytics professionals
- Interview preparation and resume positioning for entry-level analyst roles
Lesson content and order may vary based on instructor approach and changes in the industry. A standard Microsoft 365 subscription with Excel is required. Power BI (free version) and all other tools used in the program are free.
Who this is for
Who this data analytics course is for
Career changers
Moving into a data analyst role from operations, marketing, healthcare administration, finance, customer service, or another field. Your industry knowledge becomes a differentiator, because you understand the business behind the numbers.
Accidental analysts
Already pulling reports, maintaining spreadsheets, building dashboards, or fielding data requests from leadership without formal training? Get the methodology, the tools, and the credential to do that work at an analyst level.
Job seekers and returning workers
Out of work or returning to the workforce and want skills that appear in analyst job postings across nearly every industry. WIOA, DVR, and scholarships can lower or cover tuition.
Why it works
Small live cohorts with real, specific feedback
When a SQL query returns the wrong rows or a Pandas function will not behave the way the documentation says it should, you ask and get a direct answer in the moment. Small cohorts mean your work gets specific feedback from your instructor rather than a score from an automated grader, and instructors adjust examples and exercises to where students are struggling.
Career outcomes
Data analytics jobs and careers
Analytics hiring managers want to see what you can do. You finish with SQL queries, Python notebooks, Excel and Power BI reports, and a complete Capstone analysis you can walk through in an interview, plus career coaching to help you land the first role.
Career paths
Data analytics jobs and where this certificate leads
Data analyst roles exist in nearly every industry, because every organization that makes decisions based on data needs people who can collect, clean, analyze, and present it clearly. Open each role below to see what the job involves and how the program prepares you.
For a deeper look at what analyst roles look like day to day and what employers screen for in current job postings, read the Data Analyst Career Guide.
Data Analyst
Collects and interprets data to answer questions, solve problems, and communicate data-driven findings, using data visualization and statistical analysis.
In this program: SQL, Python, Pandas, statistics, Excel, and Power BI across Modules 1 to 5, with a full Capstone analysis in Module 7.
Business Analyst
Uses diagnostic, predictive, and prescriptive analytics to identify business shortcomings and opportunities and develop solutions that improve processes and performance.
In this program: statistics and interpretation (Module 4) and Excel and Power BI reporting (Module 5) build the analysis and the presentation skills the role depends on.
Marketing Analyst and Marketing Analytics Manager
Collects and analyzes campaign, customer, and channel data to improve the effectiveness of marketing. Marketing professionals who add SQL, Python, and dashboard skills move from reading reports to building them.
Data Visualization and BI Analyst
Designs and maintains dashboards and visual reports that make complex data quick to understand for decision-makers. Python visualization libraries in Module 3 and Power BI in Module 5 cover the core of this work.
Operations and Reporting Analyst
Tracks performance metrics, builds recurring reports, and answers data requests from leadership. If your job already includes pulling reports or maintaining spreadsheets, this is often the most direct next step.
Career progression: from analyst to analytics lead
Graduates pursue roles including data analyst, business analyst, operations analyst, marketing analyst, reporting analyst, and data coordinator. With experience and continued skill development, professionals advance into senior analyst, business intelligence analyst, analytics manager, and data engineering roles.
Prior backgrounds that transfer well
Data analysis is one of the few technical fields where industry knowledge from a non-technical background is genuinely valued. A former healthcare administrator who becomes an analyst in a hospital system brings organizational and clinical context a computer science graduate cannot replicate, and the same is true in finance, retail, logistics, education, and nonprofit operations.
Your domain experience does not disappear when you move into an analyst role. It becomes a differentiator, because you understand the business behind the numbers and can be the person in the room who backs decisions with data.
What employers look for in entry-level analysts
Employers screen entry-level analyst candidates for SQL, Excel, a visualization tool such as Power BI, and increasingly Python, along with the ability to explain findings to non-technical colleagues and to work effectively alongside AI tools. Candidates who can walk through a real project from question to recommendation stand out from candidates who only list tools on a resume, and the program is built to give you that project.
AI and analytics
How AI is changing data analytics work
Artificial intelligence is not replacing data analysts. It is changing how analysts spend their time and what they are expected to produce. Writing repetitive query variations, reformatting datasets, generating first-draft visualizations, and summarizing findings in plain language are increasingly assisted by AI tools, and analysts who use them well are faster, more thorough, and more valuable to their organizations.
In this program, AI is not a separate topic saved for the end. It runs through every module: you use AI-assisted tools to speed up data cleaning in Python, generate and debug SQL queries, and extend exploratory analysis, and Module 6 builds working proficiency in scikit-learn for classification, regression, and clustering. Throughout, the emphasis is on you as the analyst who frames the right question, evaluates the output critically, and communicates findings the business can act on.
Tuition
Data analytics certificate cost
*Example only: $7,995 tuition less a $2,000 average scholarship, with the remaining $5,995 financed over 36 months at 13.99% APR, Climb's lowest current rate; $100 registration fee applies; subject to credit approval. Average award shown as an example only; scholarships are reviewed and awarded individually, and award amounts are not guaranteed. Students need a standard Microsoft 365 subscription with Excel, which is not included in tuition; Power BI (free version) and all other tools are free. WIOA, DVR, and employer billing can also lower your cost.
Funding
Five ways to cover your tuition
GG
Great experience learning data science. Live sessions were intense but meaningful. No second was wasted.
Grace G., DWC certificate graduate
Included with tuition
Support every step of the way
Guidance from pre-enrollment to graduation, included for every student at no additional cost.
Admissions advisors
From pre-enrollment through your first day, an advisor walks you through program details, funding, and scheduling.
Instructor mentoring
Your instructor is your mentor before, during, and after class, helping you become industry-ready.
Student support
A dedicated student affairs manager helps you set up accounts, check your technology, and get class files.
Career coaching
Meet one-on-one with a career coach on your resume, LinkedIn, portfolio, job search, and interviews.
Tech support
Stuck mid-program? Reach tech support by Slack, email, or phone and get moving again.
Internships and alumni
Join the Parker Dewey micro-internship network for real project experience, plus the DWC alumni community.
Since 2006
Why DWC trains differently
DWC has delivered live, instructor-led career training nationwide since 2006. Data analytics is not a course added to follow a trend. It is a program built and refined for adults making a career transition, with instruction that moves at a pace you can sustain alongside work and family, portfolio work you can actually use, and direct access to an instructor who knows your name.
Your instructor frames feedback around your professional background and target roles, so years of prior experience read clearly to a hiring manager in a new field. You graduate with the DWC Data Analytics Certificate, a professional certificate issued by Digital Workshop Center, with career coaching included for resume positioning, your LinkedIn profile, portfolio presentation, and interview preparation.
FAQ
Data analytics certificate questions, answered
Still curious? Attend a free info session or call 970-980-8091.
When does the next data analytics cohort start?
The Data Analytics Certificate runs as a small live cohort on a set class schedule. Upcoming start dates, end dates, and session times (shown in MT and ET) are listed in the cohort schedule on this page, along with planned breaks. If none of the dates work for you, contact admissions and we will notify you as soon as the next cohort is scheduled.
Do I need prior coding or math experience to enroll?
No. There are no technical prerequisites. The program is designed for students starting from scratch with SQL, Python, and statistics, and each module builds from foundational concepts before moving to advanced applications. What helps most is comfort working through problems methodically and a willingness to practice between sessions. You need a computer, a reliable internet connection, a webcam, and a microphone, and DWC walks every student through a technology check before the first session.
What tools will I use in this program?
SQL for database querying, Python for analysis and scripting, Pandas for data manipulation, matplotlib, seaborn, and Plotly for visualization, Excel for analysis and reporting, Power BI for dashboards, and scikit-learn for machine learning, with AI tools used throughout. You work in Jupyter Notebooks and Google Colab, set up through Anaconda. Tools are introduced progressively rather than all at once. Excel requires a standard Microsoft 365 subscription; Power BI (free version) and every other tool are free.
How long is the program and how many hours per week does it require?
About 6 months: three live sessions per week, about 3 hours each, for 198 total class hours. Plan additional time between sessions for practice, particularly in SQL and Python, where repetition builds fluency. Most students plan for ten to twelve hours per week in total, including class time and independent practice.
How much does the Data Analytics Certificate cost?
Tuition is $7,995. You also need a standard Microsoft 365 subscription with Excel, which is not included in tuition. Power BI (free version) and every other tool used in the curriculum are free. WIOA, DVR, scholarships, employer billing, and Climb Credit payment plans can lower your out-of-pocket cost.
Can I work while enrolled in this program?
Yes. This is a part-time program designed for working adults and people who are actively job searching. Sessions are scheduled in the evenings, and the six-month timeline is structured to be manageable alongside full-time or part-time employment. Career coaching is available throughout the program, not only at graduation.
What happens if I miss a session?
Every session is taught live, and attendance matters because that is where you get answers in the moment. If you occasionally have a scheduling conflict, class recordings are available for review after each session, and you can request a class recording to catch up before the next class.
Will I build a portfolio I can show employers?
Yes. Portfolio development is built into the program from the first module rather than treated as an optional extra. Each module builds toward a project, and the Capstone is a comprehensive analysis that integrates SQL, Python, Pandas, Excel, Power BI, and machine learning into a single presentable deliverable. You leave with documented work you can walk through in detail in an interview, not just a completion certificate.
How is this different from a self-paced data analytics course?
Self-paced platforms give you video content and exercises to work through on your own. DWC is live: you attend scheduled sessions with an instructor and a small cohort of peers. When you hit a bug in your Python code or a statistics concept is not clicking, you get a direct answer in the moment from someone who can see exactly where the problem is. The Capstone is presented live with structured feedback from your instructor and peers, which produces a deeper skill set and a stronger portfolio piece.
What careers does this program prepare me for?
Graduates pursue roles including data analyst, business analyst, operations analyst, marketing analyst, financial analyst, reporting analyst, and data coordinator. SQL, Python, Excel, and Power BI are among the most commonly listed requirements in entry-level and mid-level analyst job postings across healthcare, finance, government, marketing, and logistics. The Data Analyst Career Guide breaks down roles and what employers look for.
Is this program eligible for WIOA funding?
Yes. The Data Analytics Certificate is eligible for WIOA (Workforce Innovation and Opportunity Act) funding through local American Job Centers, which can cover part or all of tuition for eligible individuals who are unemployed, underemployed, or changing careers. Eligibility is determined individually based on employment status and income. If approved, your case manager authorizes funding through an Individual Training Account, and DWC works directly with case managers, providing the program description, learning objectives, itemized tuition, the cohort schedule, and credential documentation. Apply early so funding is approved before your cohort starts. Start with our WIOA funding guide.
Can vocational rehabilitation or my employer pay for training?
Often, yes. State vocational rehabilitation (DVR) agencies fund training for people with documented disabilities who are working toward employment, and data analytics suits DVR-funded students well: the work is done on a computer, the program is 100% online, and analyst roles often offer remote options. DWC coordinates documentation, scheduling, and progress reporting with your counselor. Many employers also fund analytics training through professional development budgets or tuition reimbursement, because the data work they need done gets done faster and more accurately by someone who already knows the business. DWC invoices employers directly, offers group training for teams, and our guide on how to ask your employer to pay includes talking points and email templates.
Is there a scholarship for veterans?
Yes. The Next Mission Scholarship takes 80% off standard tuition for eligible U.S. veterans and qualifying active-duty service members within 180 days of separation. Eligibility requires verified veteran status (DD-214 or equivalent), separation within the past 7 years, and individual annual income between $30,000 and $75,000. Veterans outside that range may qualify for WIOA, VA Vocational Rehabilitation, or the Community Heroes Discount of 40% off.
What credential will I earn?
The DWC Data Analytics Certificate, a professional certificate issued by Digital Workshop Center, with full documentation of learning objectives and program outcomes for employers, workforce case managers, and DVR counselors. Analytics hiring leans heavily on demonstrated skill, so the program centers on portfolio projects and a Capstone you can present and defend rather than on a single exam.
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Reserve your seat in the next data analytics cohort.
Request program information and an admissions advisor follows up within one business day to confirm your cohort and sort out funding. You can also attend a free info session to meet the team and ask questions before you enroll.
*Class start timing depends on enrollment and cannot be guaranteed. Monthly payment example: $7,995 tuition less a $2,000 average scholarship, with the remaining $5,995 financed over 36 months at 13.99% APR (Climb's lowest current rate); $100 registration fee applies; financing subject to credit approval. Scholarships are reviewed and awarded individually, and award amounts are not guaranteed. Cohort dates and times are subject to change. An advisor follows up within one business day.