₦175,000
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Paul Adeyemi
Data Analyst / Data ScientistLead Instructor
Data & Technology
Learn Data Analysis and Become a Professional Data Analyst
Learn practical Data Analysis skills with Microsoft Excel, SQL, Python and Power BI through structured instructor-led training at VOKS Institute. Whether you are a beginner, student, graduate, working professional, entrepreneur or career switcher, this programme is designed to help you understand how data is collected, cleaned, analysed, visualised and communicated for better decision-making.
The programme combines structured lessons, practical exercises, assessments, case studies and real-world projects so that you do not simply learn software features—you learn how to use data to answer questions and solve problems.
Ready to Start Your Data Analysis Career?
If your goal is to become a Data Analyst, improve your analytical skills, build a portfolio or add data skills to your existing profession, you can start by reviewing the complete curriculum and choosing your preferred learning option.
Start Your Data Analysis TrainingWho Should Enroll in Our Data Analysis Course?
Data Analysis is useful across business, finance, education, healthcare, marketing, technology, logistics and many other industries. You do not necessarily need a computer science degree to begin. What matters is a willingness to learn, practise and work with data.
1. Students & Undergraduates
Build practical data skills before graduation, strengthen your CV and develop projects that demonstrate your ability to work with data.
2. Graduates & Job Seekers
Develop practical skills in Excel, SQL, Python and Power BI that can support your transition into data-related roles.
3. Working Professionals
Learn how to analyse business information, create reports and dashboards, and make data-driven decisions in your current role.
4. Entrepreneurs & Business Owners
Understand your sales, customers, operations and business performance by learning how to turn raw data into useful insights.
5. Career Switchers
If you want to move into technology, analytics or business intelligence, Data Analysis provides a practical starting point.
6. Anyone Interested in Data
If you enjoy solving problems, finding patterns and working with information, this course can provide a structured pathway into Data Analysis.
Course Overview
- Modules 5
- Topics 86
- Duration 12 Weeks
- Skill Level Beginner–Intermediate
- Language English
- Students 8
- Assessments Yes
- Hybrid Class Yes
- Certification Yes
Course Description
This Data Analysis course introduces learners to the role of a Data Analyst, the data ecosystem and the practical tools used to collect, prepare, analyse and communicate information.
You will learn how data can be transformed from raw information into meaningful insights that support business and professional decision-making.
The programme covers Microsoft Excel, SQL, Python and Power BI alongside data preparation, visualization, reporting, dashboard development and practical case studies.
You will also learn the difference between roles such as Data Analyst, Data Scientist, Data Engineer, Business Analyst and Business Intelligence Analyst.
Skills You Will Master
By the end of this programme, you should have a practical understanding of the core stages of the Data Analysis process.
- Explain the Data Analysis process from defining a problem to communicating insights.
- Understand different data roles including Data Analyst, Data Scientist, Data Engineer, Business Analyst and BI Analyst.
- Work with structured datasets and common data formats.
- Clean and prepare data for analysis.
- Analyse datasets using Microsoft Excel.
- Write SQL queries to retrieve and analyse database information.
- Use Python for practical data manipulation and visualization.
- Create reports and dashboards using Power BI.
- Apply data visualization techniques to communicate findings.
- Understand data modelling and relationships between tables.
- Work with realistic datasets and business case studies.
- Present analytical findings clearly to decision-makers.
- Build a portfolio of practical Data Analysis work.
Certification
Students who successfully complete the programme and meet the institute's requirements receive certification from VOKS Institute.
The training is designed to help you demonstrate practical competence in collecting, cleaning, analysing, interpreting and presenting data.
Your certificate should complement your practical portfolio and demonstrated skills rather than serve as a replacement for actual project experience.
Career Opportunities After Data Analysis Training
Data is used across finance, banking, marketing, healthcare, education, logistics, technology, government and many other industries. Developing practical analytical skills can therefore support a range of career directions.
- Data Analyst
- Business Analyst
- Business Intelligence Analyst
- Reporting Analyst
- Data Visualization Specialist
- Financial Analyst
- Marketing Analyst
- Operations Analyst
- Risk Analyst
- Data and Reporting Specialist
- Junior Data Scientist
- Business Intelligence Specialist
Data Analysis skills can also complement existing careers. Professionals in accounting, finance, marketing, sales, administration, education and entrepreneurship can use analytical skills to make better decisions with data.
Practical Data Analysis Projects
Practical experience is essential when learning Data Analysis. Throughout the programme, learners can apply concepts to realistic datasets, business questions and case studies.
- Excel data cleaning project
- Sales analysis dashboard
- Customer data analysis
- SQL database analysis project
- SQL joins and reporting project
- Python data cleaning exercise
- Python data visualization project
- Power BI dashboard
- Data modelling exercise
- Business performance report
- Customer churn case study
- Final practical portfolio project
The purpose of these projects is to help you move from simply understanding concepts to actually applying Data Analysis techniques to problems.
New to Data Analysis?
If you are completely new to Data Analysis, start by understanding what Data Analysis is, the stages involved and the tools commonly used.
Read the VOKS Data Analysis Introduction Where Can I Learn Data Analysis in Akure?Data Analysis Training in Akure and Online Across Nigeria
VOKS Institute provides Data Analysis training for learners who want to study physically in Akure or participate through live online learning. This makes the programme suitable for students and professionals within Akure as well as learners outside the city.
Learners in places such as Ondo, Owo, Ore, Akoko, Idanre, Ado-Ekiti, Osogbo, Ilesa, Ibadan, Lagos and other parts of Nigeria can explore the online learning option.
Physical Data Analysis Training
If you prefer classroom learning, direct instructor interaction and a structured physical environment, explore our guide to physical Data Analysis training in Akure.
Learn Data Analytics Physically in AkureCompare Training Options
If you are still researching where to learn Data Analysis in Akure, use our detailed guide to understand what to look for before enrolling.
Where Can I Learn Data Analysis in Akure?Why Learn Data Analysis at VOKS Institute?
Choosing a Data Analysis training institute should be based on the quality of the curriculum, practical training, instructors, learning format, projects and overall learning experience.
Practical Curriculum
Learn the core stages of Data Analysis through Excel, SQL, Python and Power BI, supported by practical exercises and case studies.
Experienced Instructors
Learn from instructors with practical experience in Data Analysis and related technology fields.
Practical Projects
Apply what you learn to datasets, dashboards, reports and case studies instead of relying only on theory.
Physical & Online Learning
Choose a learning format that works for you, whether you prefer physical training in Akure or live online classes.
Helpful Data Analysis Resources
Before enrolling, it is useful to understand the different training options, costs and learning environments available. These supporting resources can help you make a more informed decision.
Where Can I Learn Data Analysis in Akure?
A detailed guide explaining what to look for when choosing a Data Analysis training programme in Akure.
Read the GuideBest Data Analysis Training Institute in Akure
Learn the important factors to compare when evaluating Data Analysis training institutes.
Compare Training InstitutesPhysical Data Analysis Classes in Akure
Learn more about classroom-based Data Analysis training in Akure, including what to consider before enrolling.
Explore Physical TrainingData Analysis Training Cost in Akure
Understand the factors that can influence Data Analysis training costs and what to consider when comparing programmes.
Read About Data Analysis Training CostProfessional Certificate in Data Analytics Curriculum
This revised 12-week, beginner-to-intermediate programme develops practical data analytics skills through instructor-led lessons, guided practice, technical projects and an integrated capstone. The programme contains 10 modules and focuses on Excel, SQL, Python, Power BI, statistics, business analysis, documentation and responsible use of AI. Select a module to view its topics, practical activities, assessment and expected deliverables.
Programme at a glance
Duration: 12 weeks | Proposed learning hours: 144 | Modules: 10 | Mandatory projects: 5
- 1.1 Introduction to Data and Data Analytics
Meaning of data; data, information and insights; structured, semi-structured and unstructured data; qualitative and quantitative data; organizational data sources; data analytics for decisions; industry applications. - 1.2 Types of Data Analytics
Descriptive, diagnostic, predictive and prescriptive analytics; associated business questions and examples. - 1.3 Careers and the Modern Data Ecosystem
Data Analyst, BI Analyst, Business Analyst, Data Scientist, Data Engineer and Reporting Analyst; cross-functional collaboration; analytics technology landscape. - 1.4 Data Analytics Lifecycle
Problem definition; data requirements; collection; cleaning; exploration; analysis; visualization; interpretation; stakeholder communication; monitoring. - 1.5 Analytical Thinking and Problem Solving
Stakeholder objectives; analytical questions; KPIs; measures and dimensions; variables; assumptions; analytical limitations; evidence-based conclusions. - 1.6 Data Quality, Ethics and Privacy
Accuracy, completeness, consistency, validity and uniqueness; privacy and confidentiality; bias; data governance introduction.
Practical activities: Classify example datasets, define business KPIs and translate a vague scenario into an analytical brief.
Assessment / project: Business Analytics Problem-Framing Exercise
Expected deliverables: Problem statement, analytical questions, relevant data fields, KPIs and proposed approach.
- 2.1 Introduction to Microsoft Excel
Spreadsheets in business; workbooks and worksheets; rows, columns and cell references; relative, absolute and mixed references; formatting; Excel Tables; CSV and workbook import/export. - 2.2 Data Entry, Preparation and Cleaning
Data types; sorting/filtering; data validation; duplicates; missing entries; inconsistent data; Find and Replace; Text to Columns; Flash Fill. - 2.3 Essential Excel Functions
SUM, AVERAGE, COUNT, COUNTA, MIN, MAX, ROUND, ROUNDUP, MEDIAN, STDEV.S; IF, IFS, AND, OR, IFERROR; SUMIF(S), COUNTIF(S), AVERAGEIF(S). - 2.4 Text and Date Functions
LEFT, RIGHT, MID, LEN, TRIM, UPPER, LOWER, PROPER, CONCAT, TEXTJOIN, FIND, SEARCH, SUBSTITUTE; TODAY, NOW, DAY, MONTH, YEAR, DATE, EOMONTH, DATEDIF and reporting periods. - 2.5 Lookup and Reference Functions
VLOOKUP; HLOOKUP as legacy awareness; XLOOKUP; INDEX/MATCH; lookup errors; structured references. - 2.6 Modern Excel Features
Dynamic arrays (FILTER, SORT, UNIQUE, where supported); Power Query imports, transformations, combining files and repeatable cleaning. - 2.7 PivotTables and PivotCharts
PivotTables; grouping and filtering; value settings; calculated fields; PivotCharts; slicers/timelines; refresh. - 2.8 Visualization and Dashboards
Chart selection; column/bar/line/pie/scatter charts; conditional formatting; sparklines; KPI summaries; interactivity; design and interpretation.
Practical activities: Clean retail transactions, calculate KPIs, merge information using lookups and develop a PivotTable-based dashboard.
Assessment / project: Project 1 — Retail Sales Performance Analysis
Expected deliverables: Cleaned workbook, validated calculations, interactive dashboard, and business insights report; evaluate revenue, profit, products, regions and trends.
- 3.1 Introduction to Databases
DBMS; relational and non-relational systems; SQL; MySQL/PostgreSQL setup; schemas, records, keys, table relationships and ERDs. - 3.2 SQL Query Fundamentals
SELECT, DISTINCT, FROM, WHERE, ORDER BY, LIMIT, aliases, arithmetic, comparison/logical operators, IN, BETWEEN, LIKE and NULL. - 3.3 Functions and Aggregation
COUNT, SUM, AVG, MIN, MAX, GROUP BY, HAVING, conditional aggregation, CASE, COALESCE and NULLIF. - 3.4 Multiple Tables
INNER/LEFT/RIGHT joins; FULL OUTER JOIN concepts and dialect support; self joins; multi-table joins; keys and duplicated rows; aggregate correctness. - 3.5 Intermediate SQL
Subqueries; CTEs and chained CTEs; UNION and UNION ALL; string/date functions; casting; views; reusable queries. - 3.6 Window Functions
OVER, PARTITION BY, ORDER BY; ROW_NUMBER, RANK, DENSE_RANK, LAG, LEAD; running totals and moving averages. - 3.7 SQL for Business Analysis
Monthly sales, purchase frequency, ranking, average order value, segmentation, period comparisons, retention and introductory cohorts. - 3.8 SQL Best Practices
Query readability and comments, formatting, troubleshooting, introductory indexes and execution plans, validation.
Practical activities: Run analytical queries across customers, orders, products, payments and order-items tables.
Assessment / project: Project 2 — E-commerce Database Analysis
Expected deliverables: SQL scripts with at least ten explained queries, including joins, CTEs and window functions, plus insights and recommendations.
- 4.1 Statistical Foundations
Statistics; descriptive and inferential approaches; populations, samples, variable types and measurement levels. - 4.2 Descriptive Statistics
Mean, median, mode, range, variance, standard deviation, quartiles, percentiles, interquartile range and z-scores. - 4.3 Distributions
Frequency distributions, histograms, box plots, skewness, normal-distribution basics and outliers. - 4.4 Probability and Sampling
Basic probability; random variables; random sampling; sampling methods; selection bias and sampling variability. - 4.5 Correlation and Relationships
Positive and negative correlations; Pearson correlation; scatter plots; correlation versus causation; confounders. - 4.6 Introductory Statistical Inference
Point estimates, confidence intervals, hypotheses, p-values, practical vs statistical significance, Type I/II errors, introduction to A/B tests. - 4.7 Practical Statistical Analysis
Statistical summaries in Excel and Python; group comparisons; uncertainty; limits of inference.
Practical activities: Calculate descriptive statistics, compare groups, visualize distributions and cautiously interpret a confidence interval.
Assessment / project: Statistical Analysis of Customer or Sales Data
Expected deliverables: Statistical report with calculations, plots, assumptions, interpretation and limitations.
- 5.1 Introduction to Python
Why Python; installation, environments and packages; Jupyter Notebook; VS Code; running scripts. - 5.2 Syntax and Types
Variables; numeric, string and Boolean types; conversion; arithmetic/comparison/logical operators; I/O. - 5.3 Python Data Structures
Lists, tuples, sets, dictionaries, indexing, slicing, mutation and iteration. - 5.4 Control Flow
if/elif/else; for/while; break/continue; comprehensions; Problem Solving exercises. - 5.5 Functions and Reusable Code
Functions, parameters, return values, scope, built-ins and modules. - 5.6 Files and Libraries
Importing packages; text, CSV and JSON handling; paths; exceptions; NumPy arrays and numerical operations.
Practical activities: Write small scripts for business calculations; read CSV; process dictionaries and create reusable functions.
Assessment / project: Python Business Metrics Calculator
Expected deliverables: Documented notebook with tested calculations for totals, averages, growth rates and business KPIs.
- 6.1 pandas Introduction
Series, DataFrames, CSV/Excel/JSON import and dataset inspection. - 6.2 Data Exploration
head, tail, info, describe, shape, dtypes, missingness and uniqueness. - 6.3 Cleaning and Preparation
Missing values, duplicates, data types, renaming, replacements, strings, datetimes, outliers and validation. - 6.4 Manipulation
Selection, filtering, sorting, calculated columns, groupby, aggregations, pivots, merge/join, concatenation. - 6.5 Exploratory Data Analysis
Questions, summaries, patterns, trends, anomalies, distributions, comparisons and correlation. - 6.6 Visualization
Matplotlib line, bar, histogram, scatter; Seaborn count, box, heatmap, distribution and relationship charts. - 6.7 Reproducibility
Notebook organization, explanatory commentary, separation of cleaning and analysis, output checking, project documentation.
Practical activities: Import messy customer records; clean and join datasets; run exploratory analysis; create narrative visualizations.
Assessment / project: Project 3 — Customer Behaviour Analysis
Expected deliverables: Documented Jupyter notebook, cleaned dataset, charts, five evidence-based insights, recommendations and README.
- 7.1 Business Intelligence
BI vs reporting; Power BI Desktop and Service; ecosystem; installation and navigation. - 7.2 Data Connections
Excel, CSV, SQL and folder import; Import versus DirectQuery; refresh concepts. - 7.3 Power Query
Profiling, cleaning, types, split/merge, append/merge queries, conditional columns, applied steps and introduction to M. - 7.4 Data Modelling
Semantic models; fact/dimension tables; star schema; keys; relationships, cardinality, cross-filter direction, date tables and validation. - 7.5 Introductory DAX
Measures vs calculated columns; calculated table concepts; SUM, AVERAGE, COUNTROWS, DISTINCTCOUNT, DIVIDE. - 7.6 Intermediate DAX
CALCULATE, FILTER, IF, SWITCH, VAR; row/filter context; basic context transition; percentages, YTD, previous-period and growth measures. - 7.7 Visualization and Dashboard Development
Tables, matrix, bar/column, line/area, cards, KPI and maps; slicers, drillthrough, tooltips, bookmarks, navigation; accessibility, formatting and mobile-friendly layouts. - 7.8 Publishing and Management
Publish to Service; reports/dashboards, workspaces, permissions, sharing, scheduling refresh, gateways, row-level security, licensing considerations. - 7.9 Power BI Best Practices
KPIs, naming, relationships, efficient calculations, measure validation, visual clarity and report documentation.
Practical activities: Build a star schema and DAX measures from company sales data, then design multi-page management reporting.
Assessment / project: Project 4 — Business Performance Dashboard
Expected deliverables: PBIX report, documented star schema, five or more validated DAX measures, interactive reports and key findings.
- 8.1 Requirements and Analytical Questions
Stakeholder needs, business objectives, questions, KPIs, required variables and analytical scope. - 8.2 Business Performance Analysis
Revenue, margins, growth, acquisition, retention, churn, conversion, AOV, actual vs target and basic cohorts. - 8.3 Interpretation
Trends, anomalies, segment differences, possible drivers, assumptions, confounders, limitations and recommended actions. - 8.4 Data Storytelling
Audience, communication structure, executive summaries, visual evidence, chart ethics, concise recommendations and Q&A.
Practical activities: Interpret a sample management dashboard and deliver a five-minute findings briefing.
Assessment / project: Business Insights Presentation
Expected deliverables: Presentation connecting metrics, evidence, uncertainty, implications and recommended actions.
- 9.1 Professional Workflows
Project structures, raw/processed data separation, naming, transformation logs, dictionaries, assumptions, reproducibility and review. - 9.2 Data Governance
Governance, ownership, stewardship, quality, classification, access, privacy, security, retention and ethics. - 9.3 Version Control
Git and GitHub fundamentals; repositories, commits, README, collaboration, review and secrets awareness. - 9.4 AI for Data Analytics
AI-assisted formulas, SQL, Python, EDA and reporting; independent validation; hallucinations; privacy and responsible use. - 9.5 Modern Analytics Infrastructure
Data warehouses, pipelines, ETL/ELT, cloud platforms, transformations and relationships between source systems and BI.
Practical activities: Create documented repositories and data dictionaries; evaluate AI-generated code against validated results.
Assessment / project: Analytics Documentation and Quality Review
Expected deliverables: Project README, data dictionary, quality checklist, reproducible folder structure.
- 10.1 End-to-End Capstone
Define a meaningful business problem; perform cleaning and validation; analyse the data; create visualizations; document and present results. - 10.2 Project Stages
Stage 1: define objectives, questions and KPIs; Stage 2: understand, clean and document data; Stage 3: apply SQL/Python and verify outputs; Stage 4: produce a Power BI report; Stage 5: report insights and limitations; Stage 6: defend methods and findings. - 10.3 Portfolio Development
Choose projects, organize files, write READMEs, prepare concise project descriptions and publish permissible work to GitHub. - 10.4 Career Preparation
Entry-level role families, interpreting job ads, skills-focused CVs, presenting project results, common interview questions in Excel, SQL, Python and Power BI.
Practical activities: Present a complete analytics workflow for an unfamiliar business case.
Assessment / project: Integrated Business Analytics Capstone
Expected deliverables: Problem statement, raw/cleaned data where permissible, SQL/Python files, Power BI report, data dictionary, insights report, presentation and portfolio repository.
Practical Projects and Assessment Structure
| Assessment component | Weight |
|---|---|
| Weekly practical exercises and quizzes | 15% |
| Four technical module projects | 40% |
| Final capstone project | 30% |
| Capstone presentation and defence | 15% |
| Total | 100% |
The five mandatory projects are Retail Sales Performance Analysis (Excel), E-commerce Database Analysis (SQL), Customer Behaviour Analysis (Python), Business Performance Dashboard (Power BI), and the Integrated Business Analytics Capstone.
Completion Requirements
- Attend at least 80% of instructor-led sessions.
- Submit every mandatory project.
- Achieve a minimum overall assessment score of 60%.
- Submit and defend the final capstone.
- Demonstrate individual understanding of core competencies.
Teaching Methodology
- Instructor-led teaching through explanations, discussion, demonstrations and live coding.
- Practical labs using realistic business datasets.
- Case-based learning with sales, finance, marketing, customer and operations examples.
- Independent weekly assignments and review activities.
- Instructor feedback on calculations, interpretation, documentation and communication.
- Portfolio development through documented, reproducible and shareable analytics projects.
Recommended Software and Tools
| Software / technology | Purpose | Status |
|---|---|---|
| Microsoft Excel | Spreadsheet analysis | Required |
| MySQL or PostgreSQL | Relational analytics; choose a primary dialect | Required |
| Python 3, pandas and NumPy | Programming and data wrangling | Required |
| Jupyter Notebook | Interactive programming | Required |
| Matplotlib and Seaborn | Python visualisation | Required |
| Power BI Desktop | BI modelling and reporting | Required |
| Git and GitHub | Documentation and version control | Basic use |
| VS Code | Development environment | Recommended |
| AI productivity tool | Assisted learning and verification | Optional |
Power BI Desktop requires a compatible Windows environment; training should offer a lab or remote option for macOS learners. Power BI Service features depend on licensing and organisational permissions. Modern Excel functions depend on the Excel version.
12-Week Programme Delivery Schedule
| Week | Focus | Required practical output |
|---|---|---|
| 1 | Data Analytics Foundations | Business problem brief |
| 2 | Excel Preparation | Cleaned spreadsheet and calculations |
| 3 | Excel Dashboards | Retail sales dashboard |
| 4 | SQL Fundamentals | SQL exercises |
| 5 | Intermediate SQL | E-commerce SQL project |
| 6 | Statistics | Statistical analysis report |
| 7 | Python Fundamentals | Metrics calculator notebook |
| 8 | Python Analysis | Customer behaviour EDA project |
| 9 | Power BI Modelling | Model and initial DAX measures |
| 10 | Power BI Reporting | BI dashboard project |
| 11 | Business Analytics, Governance and AI | Presentation and documentation |
| 12 | Capstone, Portfolio and Careers | Final presentation and project defence |
Why Practical Data Analysis Projects Matter
Knowing the names of Excel functions, SQL commands or Power BI features is not enough to become effective at Data Analysis. You also need to understand how to approach a problem, prepare information, analyse it, create useful visualizations and communicate what the results mean.
That is why practical exercises and case studies are important. They allow you to move from learning individual tools to applying those tools to realistic questions.
What to Look for in a Data Analysis Course
Before paying for any Data Analysis training, compare the actual learning experience rather than choosing a programme based only on price or advertising.
- A clear and structured curriculum
- Relevant tools such as Excel, SQL, Python and Power BI
- Practical exercises and datasets
- Instructor guidance and feedback
- Assessments and progress checks
- Portfolio-oriented projects
- Physical and/or online learning options
- Transparent fees and payment options
Our detailed guide explains more about how to compare Data Analysis training institutes in Akure. Read the Data Analysis Training Institute Guide .
Frequently Asked Questions
Can beginners learn Data Analysis?
Yes. Beginners can start without previous professional experience. The learning path introduces concepts progressively before moving into practical tools and projects.
Do I need a computer science degree?
No. Data Analysis is not limited to computer science graduates. People from business, finance, accounting, sciences, social sciences and other backgrounds can develop Data Analysis skills through structured training and practice.
What tools will I learn?
The published curriculum covers Microsoft Excel, SQL, Python and Power BI , alongside data cleaning, analysis, visualization, dashboards and data modelling.
Can I learn Data Analysis physically in Akure?
Yes. VOKS Institute provides physical Data Analysis training in Akure. You can also read our dedicated guide: Learn Data Analytics Physically in Akure .
Can I learn online if I live outside Akure?
Yes. Live online training is available for learners who cannot attend the physical training location.
How long is the Data Analysis course?
The current published VOKS Institute Data Analysis course page lists the programme duration as 12 weeks.
Will I work on practical projects?
Yes. The programme includes practical exercises, case studies and project-based learning designed to help you apply the skills you learn.
Will I receive a certificate?
Yes. Students who successfully complete the programme and meet the institute's requirements receive certification.
How much does Data Analysis training cost?
Training costs can vary between programmes. The current VOKS Institute Data Analysis course page displays a course price of ₦175,000 and states that three instalment payments are accepted. Always check the current course page before making payment because fees may change.
Start Your Data Analysis Learning Journey
If you are ready to build practical Data Analysis skills, the next step is to review the current programme details, curriculum, learning options and registration process.
Whether you want to learn Data Analysis physically in Akure or through live online classes, VOKS Institute provides a structured pathway covering Excel, SQL, Python and Power BI.
Get Started With Data Analysis Review Course DetailsReady to Learn Data Analysis?
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