Course Overview: Welcome to the exciting world of data analytics! In this course, we will take you on a journey through the fundamentals of data analytics, providing you with the knowledge and skills necessary to harness the power of data for making informed decisions, solving complex problems, and uncovering valuable insights.
Course Objectives: Introduction to Data Analytics: Gain a solid understanding of what data analytics is and how it is used in various industries.
Data Types and Sources: Explore different types of data, including structured and unstructured data, and learn where to source data for analysis.
Data Collection and Preparation: Learn the techniques and best practices for collecting, cleaning, and preparing data for analysis.
Data Visualization: Discover the art of data visualization to effectively communicate your findings to a wider audience.
Descriptive Analytics: Dive into the world of descriptive analytics, where you will learn how to summarize, interpret, and gain insights from data.
Exploratory Data Analysis (EDA): Master EDA techniques to understand data patterns and relationships.
Inferential Statistics: Explore statistical methods for drawing meaningful conclusions and making predictions from data. Hypothesis Testing: Learn how to test hypotheses and make data-driven decisions.
Course Format: Self-Paced Learning
- This course is delivered through videos tutorials
By the end this program, you should be able to:
Understand Data Analytics: Develop a comprehensive understanding of what data analytics is and its applications in various industries, enabling you to appreciate its significance in decision-making and problem-solving.
Identify Data Types and Sources: Recognize different data types, including structured and unstructured data, and know where and how to access and collect data for analysis. Collect and Prepare Data: Acquire the skills and best practices for gathering, cleaning, and preparing data, ensuring it is suitable for analysis.
Create Effective Data Visualizations: Master the art of data visualization to create compelling and informative charts and graphs that effectively convey your insights to a broader audience.
Summarize and Interpret Data: Proficiently summarize and interpret data using descriptive analytics techniques, allowing you to draw valuable insights from your datasets.
Conduct Exploratory Data Analysis (EDA): Apply EDA techniques to uncover data patterns, relationships, and anomalies, facilitating deeper understanding of your data.
Apply Inferential Statistics: Explore a variety of statistical methods that enable you to draw meaningful conclusions and make predictions from data, giving you a strong foundation in statistical analysis.
Perform Hypothesis Testing: Skilfully design and execute hypothesis tests to make data-driven decisions, ensuring the validity and reliability of your analytical results.
Utilize Regression Analysis: Understand the principles of regression analysis, enabling you to build models for prediction and understanding the relationships between variables.
Analyse Time Series Data: Gain insights into time-dependent data and develop the ability to conduct time series analysis, including forecasting future trends and patterns.
Explore Machine Learning: Be introduced to the fundamentals of machine learning techniques used in data analytics, providing you with the knowledge to apply these methods to solve real-world problems.
Practice Data Ethics and Privacy: Understand the ethical considerations when working with data, ensuring that your data analytics activities comply with privacy regulations and ethical standards.
Apply Data Analytics in Real-World Scenarios: Explore practical examples and case studies of data analytics applications across various industries, allowing you to apply your newfound skills in real-life contexts.
Upon completing this course, you will have a strong foundation in data analytics, equipping you with the essential knowledge and skills needed to leverage data for informed decision-making, problem-solving, and gaining valuable insights in today’s data-driven world.
Course Features
- Lectures 34
- Quizzes 1
- Duration 6 Months
- Skill level Beginner
- Language English
- Students 1
- Certificate No
- Assessments Yes