Data Analytics is a multifaceted field that revolves around the process of examining, cleaning, transforming, and interpreting data to extract valuable Insights(information), patterns, and trends from it. These insights are then used to make strategic decisions, solve complex problems, and optimize various aspects of business, science, and everyday life. Data analytics entails:

1. Data Collection: Data analytics begins with the collection of raw data from diverse sources. These sources can include structured data from databases, spreadsheets, or transactional records, as well as unstructured data from social media, text documents, images, or sensors. The collected data may be vast and come in various formats.

2. Data Cleaning and Preprocessing: Raw data often contains errors, inconsistencies, missing values, or duplications. Data analysts engage in data cleaning and preprocessing to ensure the data is accurate and ready for analysis. This step involves data cleansing, transformation, and handling missing data.

3. Data Storage and Management: Storing and managing data is a crucial part of data analytics. It involves using databases, data warehouses, and cloud solutions to organize and make data easily accessible for analysis. Data security and privacy are also integral aspects of data management.

4. Data Analysis: Once data is cleaned and stored, the next step is data analysis. Analysts use a variety of statistical, mathematical, and computational techniques to identify patterns, correlations, and anomalies in the data. This analysis helps answer specific questions or address particular challenges.

5. Data Visualization: Data visualization plays a pivotal role in data analytics. By representing data in visual forms such as charts, graphs, and dashboards, analysts make complex data more accessible and comprehensible to non-technical stakeholders. Visualization aids in conveying insights and trends effectively.

6. Statistical and Machine Learning Models: Data analytics often employs statistical methods and machine learning algorithms to make predictions, classifications, and recommendations. These models are trained on historical data and applied to new data to forecast future trends or identify opportunities and risks.

7. Descriptive, Predictive, and Prescriptive Analytics: Data analytics encompasses different types of analytics, including:
– Descriptive Analytics: Summarizes historical data to understand past events and their causes.
– Predictive Analytics: Uses historical data and statistical or machine learning models to forecast future outcomes.
– Prescriptive Analytics: Offers recommendations or actions based on predictions, helping organizations make informed decisions.

8. Business Intelligence (BI): Data analytics often overlaps with Business Intelligence (BI). BI tools and platforms enable users to create reports, dashboards, and scorecards to monitor key performance indicators and support data-driven decision-making.

9. Real-World Applications: Data analytics has wide-ranging applications across industries, including marketing, finance, healthcare, supply chain management, and scientific research. It’s used to improve customer experiences, optimize operations, detect fraud, identify trends in scientific data, and much more.

10. Ethical and Legal Considerations: As data analytics deals with personal and sensitive data, ethical and legal concerns are critical. Privacy regulations like GDPR (General Data Protection Regulation) and ethical guidelines are important aspects of responsible data analytics.

Finally, data analytics is a powerful tool for converting data into actionable insights. It has the potential to revolutionize decision-making, improve processes, and foster innovation. As organizations and individuals continue to collect and store massive amounts of data, data analytics remains a critical skill and practice for harnessing the hidden potential within that data.

1. Data Analytics Essentials:
a. Discover the key pillars of data analytics, including Predictive (predicting future outcomes), Prescriptive (recommendations for actions), and Descriptive (summarizing past data) Analytics.
b. Explore different storage structures, such as Databases and Data Warehouses, to manage and organize your data effectively.
c. Gain insights into the crucial choice between Cloud and Legacy IT Infrastructure options to support your analytics projects.
d. Introduction to MySQL, a popular relational database management system (RDBMS) for storing and managing data.

2. Relational Database Management Systems (RDBMS) Concepts:
a. Understand the various data types used in databases and how they impact data storage and retrieval.
b. Explore the fundamental components of a relational database, including Fields, Rows (or Records), and Columns, to structure and manage your data efficiently. Learn how these concepts apply to MySQL.

3. Microsoft Excel:
a. Begin with an introduction to Excel, the powerful spreadsheet tool, and learn how to use it for data analysis.
b. Master the art of creating Tables, Pivot Tables, and creating visually compelling Graphs and Charts.
c. Dive into advanced features like Data Models, PowerPivot, and PowerQuery for more sophisticated data manipulation.
d. Enhance your skills with Advanced Excel Formulas to perform complex calculations and analyses.

4. MySQL:
a. Learn the basics of MySQL, including database creation and management, and SQL for data retrieval and manipulation.
b. Explore MySQL-specific concepts like tables, indexes, and primary keys.
c. Gain proficiency in writing SQL queries to extract, transform, and analyze data in MySQL databases.

5. Microsoft Power BIz:
a. Learn how to connect and import data from various sources into Power BI for in-depth analysis.
b. Harness the power of Power Query for data extraction and transformation, making your data ready for analysis.
c. Develop expertise in creating stunning visualizations and designing interactive dashboards to communicate your findings effectively.
d. Explore Data Analysis Expressions (DAX) for performing sophisticated data analysis within Power BI.

2 – 3 hours / Session
3 Session / Week for 12 Weeks

On-site: N250,000
Online Classes: In view

1. No prior experience in data analytics is required. However, a basic understanding of mathematics and familiarity with spreadsheet software (e.g., Excel) is recommended.

2. A Student is expected to have a stable Computer laptop of minimum RAM (random access memory) of 8gb and an operating system of 32bit/ 64bit (windows 7,8 or 10).

3. A Student should be able to read and understand English Language.