Training Success Elements

  • Earn an industry-recognized Data Analytics certification in 2 semesters and earn 6 credits.
  • Do hundreds of AI-mentored mini projects to build your competency and confidence and give you a feeling of accomplishment.
  • You’re never left alone when doing work — our AI-based teaching platform is there throughout your training, helping you in realtime.
  • Be ready to take the CompTIA’s Data+ industry-recognized professional certification at graduation.
  • Study anywhere, any time — all you need is a computer and an internet connection.

Complement Your Degree With A High-Demand Skill.

Data Analytics has become a core competency for enterprises because it drives decision-making, enhances efficiency, and provides a competitive advantage. Organizations that effectively use data can improve operations, optimize strategies, and adapt to market changes faster.

For over 15 years, we have been perfecting a gamified, learn-by-doing approach that ensures that you gather the skill set and practical experience that employers want job applicants to have.

Overview of Training

This gold star training program is designed for you, if you are wanting to acquire a high-demand skill, alongside your degree. It provides you with enough practice and theoretical knowledge to make you job-ready when you graduate.

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 your 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.

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 training begins with comprehensive data literacy courses, in which you learn all about:

  • Data sources
  • Data types
  • Data relationships
  • Data structures
  • Sorting, filtering and grouping, data and
  • Other fundamental techniques of Data Analytics.

You then learn basic and advanced spreadsheet operations using Google Sheets. You also learn all about data visualization, statistical methods, data analysis, and data mining. Finally, you learn how to create interactive dashboards. Upon completing this course, you have real experience acquiring data, storing it, manipulating it, analyzing it, and using it to make better business decisions.

Coursework

The program consists of two semesters, Data Analytics I and Data Analytics II, and it fully prepares you to take the industry-recognized CompTIA DATA+ Data Analytics certification exam. Following is a description of modules in each semester course.

Data Analytics I — Semester 1

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.

Click here to access the Data Analytics I syllabus and use the links in the table of contents to see the various topics that you will be expected to master.

Data Analytics II — Semester 2

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.

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

Click here to access the Data Analytics II syllabus and use the links in the table of contents to see the various topics that you will be expected to master.

Get More Information

To talk with one of our product specialists about this program, schedule a call below, fill in the Get-In-Touch form, or simply use the CHAT BOX at the bottom right of this screen.

What Our Learners Say About Us

Challenging and fun

“This course feels like more of a challenge rather than a class, a lot more fun!” J. S.

Great flexibility

“I love the self-taught aspect of the course supplemented by the teacher. Instruction is excellent. The flexibility is superb.” A. R.

Well organized

“The feature I find most beneficial about this course format is that the notes are always one click away so I don’t need to keep track of writing down my own notes. I also like how we are able to learn at our own pace.” A. L.

Straight to the point

“The most beneficial parts of this hands on course is the ability to access the instructor and the strait-to-the-point lecture and side notes.” E. M.

Perfect teaching method

“As an adult with a busy schedule and the need to work around banking business hours etc.: this method of teaching is perfect for me and I would recommend it for basically anyone on any subject.” F. V.

Superb way to learn

“I really enjoyed the self-paced format of this training program. The material was presented clearly and concisely and allowed me to focus longer on concepts that were difficult, while glancing over concepts I was already familiar with.” A.C.

I really learned a lot

“I really learned a lot and have found a new passion towards data science and analytics I didn’t know I had.” M.N.

This is how I like to learn

“This is how I like to learn, at my own pace. I like the bite-sized tutorials and I feel a real sense of accomplishment when I complete the exercises that follow them.” M.J.

Totally worth it

“I just completed 3 courses: SQL Fundamentals, Advanced SQL, and Predictive Data Analytics with Python within the estimated hours that they had advised. It was a lot of work, but totally worth it!” Q.B

Great training!

“I watched videos, read short tutorials, viewed examples, ran demo programs, and then performed exercises that demonstrated my comprehension of the subject matter.” S.L.

The training is really great!

“Not easy but really informative. Practical experience was what I wanted and I feel that I got it.” F. Z.

The program is great

“I cannot compliment enough any training that involves students actually working on the problems rather than just listening.” A. S.

I love my new job!

“My boss encouraged me to take the training and now I’m really glad I did because I’m now doing data analytics work full time.” L.L.

You do a great job explaining the concepts

“So far I’m enjoying the course. You do a great job explaining the concepts and the logic behind each step (the explanation for the difference between WHERE and HAVING comes to mind)!” R. C.

I’m really enjoying this class

“The emphasis on working at your own pace removes the stress of a normal class, and I can work around my other classes. NCLab is much better than similar programs, such as DataCamp. Not only do I learn the material, I retain it. Each lesson uses all or most of the previous concepts, which provides additional practice and ensures that you don’t forget the previous material. This is one of the most enjoyable and informative programs I’ve taken.” N. L.

I love going to work every day!

“Thank you for giving me the competency and confidence that I needed to get a dream job. With my limited formal education, I had serious doubts that I could master SQL and Python but learning by doing worked perfectly.” G.K.

 

 

 

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