DAF-101
| Formats: | Asynchronous |
| Blended | |
| Online | |
| Onsite | |
| Part-time | |
| Level: | Beginner |
| Prerequisites: | |
| Recommended Knowledge | |
| Basic Computer Literacy. | |
| Basic Math & Logic Skills. | |
| Familiarity with Spreadsheets. | |
Formats: We offer our training content in a flexible format to suit your needs. Contact Us if you wish to know if we can accommodate your unique requirements.
Level: We are happy to customize course content to suit your skill level and learning goals. Contact us for a customized learning path.
Data Analytics Fundamentals: (DAF-101)
Data Analytics Fundamentals: From Data to Insight
Mastering the Core Skills for Unlocking Value from Data in South Africa
In an era defined by information, the ability to collect, process, and interpret data is no longer just for specialists – it's a fundamental skill for professionals across all industries. Data Analytics Fundamentals provides you with the essential knowledge and practical skills to transform raw data into meaningful insights that drive informed decision-making.
At Big Data Labs, located in Randburg, Gauteng, we believe that a solid foundation in data analytics is the starting point for any data-driven career. This course is designed to empower individuals in South Africa to confidently navigate the data landscape, understand key analytical processes, and extract actionable intelligence from various data sources.
Target Audience
This course is perfect for anyone looking to build a strong foundation in data analytics, including:
Aspiring Data Analysts
Individuals looking to kickstart a career in data analysis.
Business Professionals & Managers
Who need to understand and interpret data for strategic planning and operational efficiency.
Students & Career Changers
Seeking a comprehensive introduction to the data analytics field.
Marketing, Finance, & HR Specialists
Needing to apply data-driven insights to their specific domains.
Prerequisite Skills
This course is designed for beginners. No prior experience in data analytics or programming is required, but participants should have:
- Basic Computer Literacy: Familiarity with operating a computer and using common software applications.
- Basic Math & Logic Skills: An understanding of fundamental mathematical operations and logical thinking.
- Familiarity with Spreadsheets (e.g., Excel): Basic usage for organizing data is helpful but not strictly required.
What One Will Learn (Learning Outcomes)
Upon completion of this course, you will be able to:
- Understand Core Concepts: Grasp the fundamentals of data analytics and its various types.
- Prepare Data for Analysis: Clean, transform, and prepare messy datasets for accurate insights.
- Analyze Data with SQL & Excel: Utilize essential SQL queries and advanced Excel features to extract information.
- Perform Exploratory Data Analysis (EDA): Uncover patterns, trends, and anomalies in data using descriptive statistics.
- Create Effective Visualizations: Design compelling charts and dashboards to communicate findings clearly.
- Communicate Data Insights: Tell a story with data, presenting analytical findings in a clear and impactful way.
Target Market
This foundational course is vital for any organisation in South Africa that seeks to become more data-driven, regardless of sector:
Small to Large Enterprises
Empowering employees with data literacy across all departments.
Consulting Firms
Equipping consultants with the ability to perform basic data-driven client analyses.
Financial Services
Improving decision-making in areas like fraud detection, customer behavior, and risk assessment.
Retail & E-commerce
Understanding sales trends, customer preferences, and inventory management.
Healthcare
Analyzing patient data, operational efficiency, and public health trends.
Course Outline: Data Analytics Fundamentals
This course provides a comprehensive introduction to the principles, tools, and techniques essential for performing effective data analysis, from data acquisition to insight delivery.
Module 1: Introduction to Data Analytics
- What is Data Analytics? Definition, importance, and its role in business decision-making.
- Types of Data Analytics: Descriptive, Diagnostic, Predictive, and Prescriptive.
- The Data Analytics Lifecycle: From problem definition to communicating insights.
- Key Data Terminology: Metrics, KPIs, dimensions, facts, datasets.
Module 2: Data Sourcing & Understanding
- Common Data Sources: Databases (SQL, NoSQL concepts), spreadsheets, web data, APIs.
- Understanding Data Types: Numerical (continuous, discrete), Categorical (nominal, ordinal), Temporal.
- Introduction to Data Quality: Accuracy, completeness, consistency, validity, uniqueness, timeliness.
- Basic Data Privacy & Ethics: Introduction to responsible data handling.
Module 3: Data Preparation & Cleaning
- The Importance of Clean Data: "Garbage In, Garbage Out" principle.
- Handling Missing Values: Deletion, imputation (mean, median, mode).
- Dealing with Outliers: Identification and mitigation strategies.
- Data Standardisation & Normalisation: Converting data to a consistent format.
- Data Aggregation & Merging: Concatenating and joining datasets.
- Tools Introduction: Excel for basic cleaning, overview of Python/Pandas capabilities.
Module 4: Data Analysis with SQL & Excel
- Introduction to Relational Database Concepts: Tables, columns, rows, primary/foreign keys.
- Core SQL for Data Analysis: SELECT, FROM, WHERE, GROUP BY, ORDER BY.
- SQL Joins: INNER JOIN, LEFT JOIN for combining data from multiple tables.
- Aggregate Functions in SQL: SUM, AVG, COUNT, MIN, MAX.
- Advanced Excel Functions for Data Analysis: PivotTables, VLOOKUP/XLOOKUP, conditional formatting, basic formulas.
- Basic Excel Charts for Data Interpretation.
Module 5: Exploratory Data Analysis (EDA)
- Purpose of EDA: Uncovering patterns, detecting anomalies, and forming hypotheses.
- Descriptive Statistics: Mean, Median, Mode, Standard Deviation, Variance, Quartiles.
- Frequency Distributions and Histograms for understanding data spread.
- Correlation vs. Causation: Understanding relationships between variables.
- Introduction to Python/Pandas for basic EDA:
.describe(),.info(),.value_counts().
Module 6: Data Visualization & Reporting
- Principles of Effective Data Visualization: Clarity, accuracy, impact, avoiding misleading visuals.
- Choosing the Right Chart Type: Bar charts, line charts, pie charts, scatter plots, histograms, box plots.
- Introduction to Business Intelligence (BI) Tools: Concepts of Power BI and Tableau.
- Designing Effective Dashboards & Reports: Layout, interactivity, key insights.
- Storytelling with Data: Communicating insights clearly and compellingly.
Module 7: Introduction to Statistical Concepts for Analytics
- Sampling & Sampling Bias: Understanding data representation.
- Basic Probability Concepts: Understanding likelihood.
- Hypothesis Testing Fundamentals: Concepts of A/B testing and statistical significance.
- Introduction to Regression Analysis: Understanding linear relationships in data.
Module 8: Tools, Ethics & Best Practices
- Overview of the Data Analytics Tools Ecosystem: BI tools, programming languages, cloud platforms.
- Data Governance & Ethics in Practice: Ensuring responsible and compliant data use.
- Documentation & Reproducibility: Making your analysis understandable and repeatable.
- Continuous Learning & Trends in Data Analytics.