This gold star training program is designed for you, if you are determined to become a skilled Data Analyst. It provides you with enough practice and theoretical knowledge to make you job-ready when you graduate. It also teaches you the soft skills that employers look for in job applicants. Upon completing the training, you are ready to start a successful new career as a Data Analyst.

Data Analytics can’t be learned in a few weeks by passively watching video tutorials and doing unassisted assignments. It’s an advanced skill set which can only be mastered with a significant amount of closely supervised practice. NCLab’s proven training method is called Instructor-Assisted Learning By Doing.

Throughout you learning, you are assisted by a powerful Artificial Intelligence-based teaching platform that watches your every step, grading your work in real time, and helping you with contextual information, hints, and templates, as needed. The AI-based platform also teaches you established best practices, methodologies, and guidelines that you need to follow to ensure consistency, accuracy, and reliability in your work.

At the time you enroll, you are assigned a personal Career Coach who:

• Prepares a personalized roadmap and training timeline with you.
• Works with you for the entire duration of your training.
• Provides you with one-to-one assistance in getting a great job.

Because your training is self-paced and individualized to your schedule, you are able to build a consistent training routine with your Career Coach that does not interfere with your work and family commitments.

You learn actively from Day 1. After gaining confidence in one topic, you move to the next one. The progression has been improved and tuned for many years and it is so smooth that you never get lost and you are never alone.

The entire Data Analyst career training program takes approximately 320 hands-on hours.

Coursework

To begin with, given that Data Analysts work extensively with numbers, you must know some math. If, in consultation with your Career Coach, it is determined that you need a basic middle school level math refresher, our optional free Workplace Math course provides hands-on review and practice to bring you up to speed. In small and simple steps, we review how to work with whole and decimal numbers, fractions, percentages, proportions, simple and compound interest, unit conversions, and solve simple workplace-related math problems. With an appropriate math foundation in place, you begin your Data Analyst training, which consists of the following courses:

SQL Fundamentals (20 hours): This module covers essential facts about data and databases, including the difference between relational and non-relational databases, design and ethics principles, referential integrity, ACID, and the differences among various SQL flavors. You learn how to create basic queries using SELECT, ORDER BY, LIMIT, and OFFSET clauses. You also learn how to use aggregate functions such as COUNT, AVG, MIN, MAX, and SUM, filter data with WHERE, WHERE-LIKE, WHERE-BETWEEN, and WHERE-IN clauses, and combine multiple conditions using AND, OR, and NOT. Instruction includes grouping data with GROUP BY and HAVING clauses, modifying databases with ALTER TABLE, defining constraints, setting default values, and merging tables using inner joins.

Data Literacy (40 hours): You learn core data concepts and gain practical skills using spreadsheets to enter, organize, modify, and analyze data. You practice manual data entry, basic formatting, menu and keyboard shortcut use, working with cell addresses and ranges, performing copy, cut, and paste operations, importing data from CSV files and the web, filtering and sorting data, using formulas, and performing calculations. Additional topics include grouping rows and columns, applying conditional formatting, and creating data validation rules. You also learn to use functions, conditions, conditional aggregate functions, wildcards, arrays, date/time functions, information functions, error handling, text processing, and basic lookups.

Data Visualization (20 hours): This module reviews data classification as quantitative (numeric) or qualitative (categorical), emphasizes the importance of data visualization, and guides you in selecting the best visualization technique for your data. You learn how to create and modify a wide range of chart types, including line, bar, column, pie, histogram, geo, waterfall, candlestick, radar, treemap, organizational, gauge, scorecard, Gantt, sparkline, bubble, and scatter charts.

Data Analysis (20 hours): You expand your understanding of probability and statistics while gaining practical experience analyzing spreadsheet data. The focus is on simple linear regression, with additional coverage of multiple linear regression and logistic regression. Topics include variables, observations, causal relationships, independent and dependent variables, data compatibility, measures of central tendency and variability, probability concepts, discrete and continuous probability, Probability Density Functions (PDF), normal distribution, the 68-95-99.7 rule, skewness, kurtosis, correlation, and goodness of fit. You also learn about other data distributions. Hypothesis testing is covered in depth, including alternative (H1) and null (H0) hypotheses, P-values, significance levels, type I and II errors, one- and two-tailed tests, T-tests, F-tests, ANOVA, Z-tests, and Chi-squared tests.

Advanced Spreadsheets (20 hours): This module introduces advanced spreadsheet capabilities, such as lookups, named ranges, named functions, pivot tables, slicers, and data cleanup tools. You learn basic SQL and practice integrating SQL into spreadsheets using the QUERY function. The module concludes with instruction on using, creating, importing, and managing macros.

Introduction to Dashboards (20 hours): You learn principles of dashboard creation, including differences between static and dynamic dashboards and how to interpret existing dashboards for insights. You examine the four purposes of dashboards—strategic, analytical, operational, and tactical—and practice profiling, cleaning, classifying, and preparing data. The module includes coverage of sensitive data review, legal protections, masking, and de-identification techniques. You learn how to build static and dynamic dashboards and complete a capstone project to create a unique dynamic dashboard from scratch using a provided dataset and guidelines.

Excel Project (40 hours): In this module, you complete a real-world project designed to transition your skills from Google Sheets to Excel.

Dashboards in Sheets (10 hours): You practice creating dashboards in Google Sheets, applying data visualization and interactivity techniques.

Dashboards in Tableau (10 hours): This module introduces you to Tableau, teaching you how to use the platform to design and build dashboards.

Dashboards in Power BI (10 hours): You are introduced to Power BI and learn how to create dashboards using its tools and features.

Advanced SQL (50 hours): This module covers advanced SQL techniques essential for working with large, real-world databases. You learn to insert conditional expressions into queries using the CASE keyword, apply the FILTER clause to simplify value filtering in aggregate functions, and work with NULL values using NULLIF and COALESCE. You develop skills in handling text strings and regular expressions, using the ASCII table and Unicode extension, performing case-sensitive and case-insensitive searches, and executing find-and-replace operations. Additional topics include working with sets, creating subqueries, and applying advanced joins and functions.

AI-Powered Data Analytics (20 hours): This a project-driven course where you learn to leverage AI as a powerful assistant for exploring, visualizing, and interpreting data. Working with real-world datasets, you use AI to generate insights, create visualizations, and suggest analytical approaches, while critically evaluating the accuracy, relevance, and completeness of AI outputs. Through hands-on projects and iterative refinement, you strengthen your skills in data preparation, statistical analysis, and visualization, gaining a clear-eyed understanding of both the advantages and the limitations of AI in the analytics workflow. By the end, you know when to trust AI’s guidance, when to verify it, and how to make the final analytical decisions yourself.

CompTIA Data+ Exam Prep (20 hours): This module provides you with multiple realistic CompTIA Data+ practice exams to prepare you for certification success.

Capstone Project: The capstone is a hands-on project where you conduct queries. All queries are conducted in a relational database system and a suitable editor that you choose and install on your personal computer. You write and execute queries to clean and group data and obtain descriptive statistics of the data and create visualizations of your choice.

AI-Powered Job Search: As you approach the latter stages of your NCLab training, this essential short course equips you with the tools and strategies needed to land your dream job. You refine your professional assets - crafting a standout resume, portfolio, LinkedIn profile, cover letter, and elevator pitch - while leveraging AI to enhance your job search. Through hands-on guidance, you learn to find and research job listings, tailor your applications using AI, and submit three targeted applications. To ensure you're fully prepared, you participate in mock interviews, gaining confidence for real-world opportunities. Throughout the course, your Career Coach supports you every step of the way, helping you complete key tasks that set you up for success.

Program Syllabus

We invite you to look at our training program syllabus but we need to explain how it is different from other syllabi you might have or will look at.

While all syllabi show you what is taught in the training, our syllabus shows you both what you are taught and what you are required to make use of yourself. To an employer, this means that you have actually mastered each of the topics covered. Click here to access the syllabus and use the links in the table of contents to see how the various topics that you will be expected to master.

Developing Soft Skills

At the same time as you develop Data Analyst (hard) skills, you also develop soft skills during your academic advice sessions and during your communications with your instructional support team that employers put a major emphasis on. These include professional communication, attention to detail, time management, critical and logical thinking, problem solving, perseverance, and adaptability, among others.

Advanced Data Analyst Training

Upon completing the Data Analyst Training, you can either get a job as a Data Analyst or continue by taking a follow-up Advanced Data Analyst training program that makes you a job-ready Python Data Analyst. This program includes an optional Introduction To Programming course for trainees who have no prior experience with computer programming, a Python Fundamentals course, a full complement of Python topics, and a Capstone Project. More information is available upon request.

Experience NCLab's Learn-By-Doing -- You'll Love It.

Click here to see how NCLab's AI-based teaching platform is with you throughout your training, helping you do mini projects in realtime and providing you a feeling of accomplishment as you complete each mini project.

Get More Information

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