Sign up now for our January 2025 Data Analytics training cohort!
The Beginners Data Analytics course is designed to provide participants with a solid foundation in the field of data analytics. Throughout the 2-month program, you will gain essential skills and knowledge in various fundamental areas of data analysis, enabling you to kickstart your journey in the world of data-driven decision-making.
Course fee
₦ 350,000 ₦ 250,000
Start a career in data analytics as you enroll for this beginners level course
Discover the world of data analytics at your pace, with engaging pre-recorded videos designed to offer flexibility, convenience, and practical learning experiences. Elevate your skills through our dynamic weekend live sessions, led by industry veterans and seasoned data professionals, fostering interactive and engaging learning environments.
What You Will Learn;
Use tools like Excel, SQL, and Power BI to gather and analyze data
Analyze and interpret complex data sets to make informed business decisions
Present your findings in a clear and concise manner
Course Summary
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COURSE LEVEL
Data Analytics - Beginners
COURSE DURATION
2 Months + 1 Week Guided Project
MODE OF TRAINING
Online & Physical Classes
TRAINING TIME
Weekdays
Morning 10am - 12pm
Evening 3pm - 5pm
Weekend Classes
Evening 4pm - 6pm
TRAINING FEE
Online: ₦ 250,000 ₦ 150,000
Physical: ₦ 350,000 ₦ 250,000
Course outline for our Data Analytics course - beginners level
Our comprehensive curriculum is designed to equip you with the essential skills and knowledge needed to excel in the dynamic field of data analytics. Through a series of engaging courses, you'll explore foundational concepts, master industry-leading tools and techniques, and gain hands-on experience with real-world projects.
Overview of Data Analytics:
Dive into the dynamic world of data analytics and discover its significance in today's business landscape. Gain insights into how data analytics empowers organizations to make informed decisions, drive innovation, and achieve competitive advantage.
Importance of Data Analytics in Business:
Understand the pivotal role of data analytics in driving business success. Explore real-world examples of how organizations leverage data analytics to enhance operations, optimize marketing strategies, improve customer experiences, and unlock new revenue opportunities.
Introduction to Data Analysis Process:
Embark on your data analytics journey by learning the fundamental steps of the data analysis process. From defining objectives and collecting data to analyzing insights and drawing conclusions, discover the systematic approach to deriving actionable insights from data.
Understanding Data Types and Sources:
Explore the diverse landscape of data types and sources prevalent in today's digital age. Learn how to identify structured and unstructured data, understand the differences between qualitative and quantitative data, and recognize various data sources including databases, APIs, and more.
Introduction to Data Cleaning and Preprocessing:
Delve into the critical process of data cleaning and preprocessing, essential for ensuring data quality and reliability in analytics projects. Learn techniques for identifying and addressing data inconsistencies, missing values, outliers, and other data quality issues to lay the foundation for accurate analysis and insights.
Introduction to Spreadsheet for Data Analysis:
Dive into the world of spreadsheet software and discover its role in data analytics. Learn how to leverage popular platforms like Google Sheets and Microsoft Excel to organize, analyze, and visualize data effectively. Explore the interface, basic functionalities, and key features essential for data analysis tasks.
Basic Google Sheets Functions for Data Analysis:
Master the essential functions of Google Sheets for data analysis. Explore common functions for data manipulation, calculation, and transformation, including formulas for arithmetic operations, text manipulation, date and time calculations, and more. Build a solid foundation in using Google Sheets as a powerful data analysis tool.
Data Cleaning and Formatting in Google Sheets:
Learn the importance of data cleaning and formatting in the data analysis process and discover how to perform these tasks efficiently in Google Sheets. Explore techniques for identifying and resolving data quality issues, such as removing duplicates, handling missing values, and formatting data for clarity and consistency.
Data Analysis Tools and Techniques in Google Sheets:
Unlock the full potential of Google Sheets for data analysis with advanced tools and techniques. Explore built-in features and add-ons that streamline data analysis tasks, including sorting and filtering data, creating custom functions, using conditional formatting, and utilizing data validation rules.
Pivot Tables and Pivot Charts for Data Analysis:
Dive deep into pivot tables and pivot charts, powerful tools for summarizing and visualizing data in Google Sheets. Learn how to create pivot tables to analyze large datasets, aggregate data, and generate insightful summaries. Explore pivot charts to visually represent data trends, patterns, and relationships for effective data-driven decision-making.
Introduction to SQL and Relational Databases:
Embark on your journey into SQL (Structured Query Language) and relational databases. Learn the fundamentals of SQL syntax, database management systems, and the relational model. Explore the role of SQL in data analytics and its importance in querying, manipulating, and managing data.
Retrieving Data with SELECT Statement:
Master the art of retrieving data from relational databases using the SELECT statement. Dive deep into SQL syntax to select specific columns, filter rows, and sort results. Learn to craft precise queries to extract the information you need from your database tables efficiently.
Filtering and Sorting Data:
Refine your SQL querying skills by mastering filtering and sorting techniques. Explore the WHERE clause to filter rows based on specified conditions and the ORDER BY clause to sort query results. Discover how to combine filtering and sorting to extract meaningful insights from your data.
Aggregating Data with GROUP BY and Aggregate Functions:
Unlock the power of aggregation in SQL with the GROUP BY clause and aggregate functions. Learn how to group rows of data based on common attributes and perform calculations on grouped data using functions such as SUM, AVG, COUNT, MIN, and MAX. Gain the skills to analyze and summarize data at various levels of granularity.
Joins and Subqueries for Data Analysis:
Dive deeper into SQL querying techniques with joins and subqueries. Explore different types of joins (e.g., INNER JOIN, LEFT JOIN, RIGHT JOIN) to combine data from multiple tables based on specified relationships. Learn to use subqueries to nest queries within other queries, enabling complex data analysis and retrieval tasks.
Introduction to Data Visualization:
Delve into the world of data visualization and its significance in transforming raw data into actionable insights. Explore the principles of effective data visualization, including clarity, accuracy, and relevance, and learn how visual representations enhance understanding and decision-making.
Understanding Data Visualization Tools (Power BI/Tableau):
Familiarize yourself with leading data visualization tools such as Power BI and Tableau. Discover their features, capabilities, and advantages, and learn how to navigate their user interfaces to create compelling visualizations. Gain hands-on experience with these powerful tools to unleash your creativity in data storytelling.
Creating Basic Visualizations (Bar Charts, Line Charts, Pie Charts):
Learn the foundations of data visualization by mastering basic chart types. Explore how to create bar charts, line charts, and pie charts to represent data trends, comparisons, and distributions effectively. Gain insights into choosing the right chart type for different data scenarios and communicating insights visually.
Advanced Visualization Techniques (Heatmaps, Treemaps, Scatter Plots):
Elevate your data visualization skills with advanced techniques and chart types. Explore the use of heatmaps to visualize patterns and correlations in large datasets, treemaps to display hierarchical data structures, and scatter plots to identify relationships and outliers. Unlock new ways to convey complex information visually.
Dashboard Design and Interactive Reporting:
Master the art of dashboard design and interactive reporting to deliver impactful insights to stakeholders. Learn how to design visually appealing dashboards that communicate key metrics and trends at a glance. Explore interactive features such as filters, drill-downs, and tooltips to enhance user engagement and exploration of data.
Introduction to Statistics for Data Analysis:
Kickstart your journey into the world of statistics and its application in data analysis. Explore the fundamental concepts of statistics, including measures of central tendency, variability, and probability. Learn how statistics provides the foundation for making data-driven decisions and drawing meaningful insights from data.
Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation):
Dive into descriptive statistics, the essential tools for summarizing and describing data. Learn how to calculate and interpret measures such as mean, median, mode, variance, and standard deviation to gain insights into the distribution and variability of data.
Probability Distributions (Normal Distribution, Binomial Distribution):
Explore probability distributions and their role in statistical analysis. Delve into the characteristics of common distributions, including the bell-shaped normal distribution and the discrete binomial distribution. Learn how to calculate probabilities and apply distribution properties in data analysis.
Hypothesis Testing and Confidence Intervals:
Master hypothesis testing, a powerful statistical method for making inferences about population parameters based on sample data. Learn how to formulate null and alternative hypotheses, conduct hypothesis tests using appropriate statistical tests, and interpret test results. Discover how confidence intervals provide estimates of population parameters with associated uncertainty.
Correlation and Regression Analysis:
Unlock the power of correlation and regression analysis for exploring relationships between variables. Learn how to calculate correlation coefficients to measure the strength and direction of relationships, and perform regression analysis to model and predict outcomes based on predictor variables. Gain insights into interpreting regression output and assessing model performance.
Introduction to Data Storytelling:
Embark on your journey into the art of data storytelling and its transformative role in conveying insights from data. Explore how storytelling techniques can bring data to life, engage audiences, and drive decision-making. Learn the fundamentals of crafting compelling narratives that resonate with stakeholders.
Understanding the Narrative Structure in Data Storytelling:
Dive deep into the narrative structure of data storytelling and learn how to structure your data-driven narratives for maximum impact. Explore the components of a compelling story, including setting the stage, introducing characters (data), presenting conflict (insights), and delivering resolution (actionable recommendations).
Effective Data Visualization for Storytelling:
Unlock the power of data visualization as a storytelling tool. Discover how to create visually appealing and informative visualizations that support your narrative and enhance audience understanding. Learn best practices for selecting chart types, formatting visuals, and incorporating storytelling elements into your data visualizations.
Communicating Insights and Findings to Non-Technical Stakeholders:
Master the art of communicating data insights and findings to non-technical stakeholders. Explore strategies for translating complex data concepts into clear, accessible language that resonates with diverse audiences. Learn how to tailor your message to the needs and preferences of your stakeholders to drive engagement and action.
Creating Compelling Data Stories for Decision-Making:
Learn how to weave together data, insights, and storytelling techniques to create compelling narratives that drive decision-making. Explore real-world case studies and examples of effective data stories that have influenced organizational outcomes. Gain practical tips and strategies for crafting data stories that inspire action and drive positive change.
To enroll in this course, we have outlined basic prerequisites for individuals keen on joining. These requirements aim to ensure a seamless and fruitful learning journey, culminating in successful course completion. Here are the fundamental prerequisites:
Demonstrate a keen interest in data analysis.
Conduct thorough research on data analytics, confirming it as your desired career path.
Possess a reliable and functional laptop, meeting minimum system requirements.
Maintain consistent internet access.
Commit to attending classes every weekend, from 4:00 pm to 6:00 pm, throughout the training period.
Minimum System Configuration for Data Analysis:
Operating System: Windows 10 or macOS Catalina (or later)
Processor: Intel Core i5 or equivalent
RAM: 8 GB
Storage: 256 GB SSD
Internet Connection: Broadband or High-speed connection recommended for online classes and data access.
Software: Microsoft Excel, Google Sheets, or any preferred spreadsheet software. Optionally, Power BI, Tableau, or SQL tools may be utilized based on course requirements.
To enroll in this course, we have outlined basic prerequisites for individuals keen on joining. These requirements aim to ensure a seamless and fruitful learning journey, culminating in successful course completion. Here are the fundamental prerequisites:
Demonstrate a keen interest in data analysis.
Conduct thorough research on data analytics, confirming it as your desired career path.
Possess a reliable and functional laptop, meeting minimum system requirements.
Maintain consistent internet access.
Commit to attending classes every weekend, from 4:00 pm to 6:00 pm, throughout the training period.
Minimum System Configuration for Data Analysis:
Operating System: Windows 10 or macOS Catalina (or later)
Processor: Intel Core i5 or equivalent
RAM: 8 GB
Storage: 256 GB SSD
Internet Connection: Broadband or High-speed connection recommended for online classes and data access.
Software: Microsoft Excel, Google Sheets, or any preferred spreadsheet software. Optionally, Power BI, Tableau, or SQL tools may be utilized based on course requirements.
For a beginner data analyst, there are several career paths to explore, each offering unique opportunities for growth and specialization. Here are some common career paths for beginner data analysts:
Data Analyst: As a data analyst, you'll focus on collecting, processing, and analyzing data to uncover insights and support decision-making within an organization. This role typically involves working with structured and unstructured data sets, performing data cleaning and validation, and creating reports and visualizations to communicate findings.
Business Analyst: Business analysts use data analysis to identify business problems, opportunities, and trends, and to recommend solutions that improve business processes and performance. This role often involves working closely with stakeholders to understand business needs, gathering and analyzing data to inform decision-making, and translating findings into actionable recommendations.
Data Scientist: Data scientists leverage advanced statistical and machine learning techniques to extract insights from data and build predictive models that drive business value. This role requires strong programming skills, expertise in data manipulation and analysis, and a deep understanding of statistical methods and algorithms.
Marketing Analyst: Marketing analysts focus on analyzing customer data, market trends, and campaign performance to optimize marketing strategies and drive business growth. This role involves identifying target audiences, measuring the effectiveness of marketing campaigns, and providing insights to inform marketing decisions.
Financial Analyst: Financial analysts use data analysis to assess financial performance, identify investment opportunities, and mitigate financial risks within an organization. This role involves analyzing financial data, creating financial models and forecasts, and providing recommendations to support strategic financial decisions.
Healthcare Analyst: Healthcare analysts apply data analysis techniques to healthcare data to improve patient outcomes, reduce costs, and optimize healthcare delivery. This role may involve analyzing electronic health records, conducting clinical research, and developing predictive models to support medical decision-making.
Payment Plans
At Datafied Academy, we provide a versatile payment option for our data analytics courses. You have the flexibility to either pay the training fee in a single payment or opt for a two-installment plan.
Pay in installments 60%-40%
Before the commencement of the training, you'll be allowed to pay 60% of the training fee, then after 1 month into the training, you pay up the remaining 40%.
Pay in full 100%
If you have the capacity to pay up the entire training fee before the commencement of the training, then this option is for you.
Datafied Academy is a top-tier institution specializing in data analytics training, dedicated to empowering individuals with the necessary skills to succeed in the dynamic field of data analytics.
