The Data Literacy Course: Learn How to Work With Data
Read, Understand, and Analyze Data
Note: 4.4/5 (2,640 notes) 33,632 students
Instructor(s): 365 Careers
Last update: 2022-04-18
What you’ll learn
- Acquire Data Literacy
- Learn from a Professional with a Proven Track Record and Valuable Experience
- Master the Language of Data
- Interpret Data Professionally
- Become Familiar with Modern Business Analytics Techniques
- How to Use Data to Improve Business Decisions
- Advance Your Career
- Make Better and Faster Decisions Using Data
- Employ Data Effectively
- Uncover Findings and Insights Independently
- No Prior Experience is Required. The Course Is Suitable for Beginners
Being data literate means having the necessary competencies to work with data.
Regardless of your field of expertise – if you want a rewarding career path – you will certainly benefit from these skills.
Any manager or business executive worth their salt is able to articulate a problem that can be solved using data.
So, if you want to build a successful career in any industry, acquiring full data literacy should certainly be one of your key objectives.
Someone who is data literate would have the ability to:
o Articulate a problem that can potentially be solved using data
o Understand the data sources involved
o Check the adequacy and fitness of data involved
o Interpret the results of an analysis and extract insights
o Make decisions based on the insights
o Explain the value generated with a use case
You will acquire all these skills by taking this course. Together, we will expand your quantitative skills and will ensure you have a solid preparation.
The course is organized into four main chapters. First, you will start with understanding data terminology – we will discuss the different types of data, data storage systems, and the technical tools needed to analyze data.
Then, we will proceed with showing you how to use data. We’ll talk about Business Intelligence (BI), Artificial Intelligence (AI), as well as various machine and deep learning techniques.
In the third chapter of the course, you will learn how to comprehend data, perform data quality assessments, and read major statistics (measures of central tendency and measures of spread).
We conclude this course with an extensive section dedicated to interpreting data. You will become familiar with fundamental analysis techniques such as correlation, simple linear regression (what r-squared and p-values indicate), forecasting, statistical tests, and many more.
By the end of the course, you will learn how to understand and use the language of data.
Your instructor for this class will be Olivier Maugain. Very few online courses are taught by people with his professional track record. Olivier has worked in various industries, such as software distribution, consulting, and consumer goods. In his current role as Decision Intelligence Manager at a major European retailer, he supports the organization in making better and faster decisions using data.
You’re about to enroll in a course that can boost your entire career!
What are you waiting for?
Click the ‘Buy Now’ button and let’s start this exciting journey today!
Who this course is for
- People Who Want a Successful Career in Business
- Business Executives
- Ambitious Managers
- Business Intelligence Analysts
- Business Analysts
- Financial Analysts
- Anyone Who Wants to Understand How to Measure Business Performance
- What does the course cover? What is Data Literacy?
- Why do we Need Data Literacy?
- Data-driven Decision Making
- Benefits of Data Literacy
- How to Get Started?
- UNDERSTANDING DATA
- Data Definition
- Qualitative vs. Quantitative Data
- Structured vs. Unstructured Data
- Data at Rest vs. Data in Motion
- Transactional vs. Master Data
- Big Data
- Storing Data
- Data Warehouse
- Data Marts
- The ETL Process
- Apache Hadoop
- Data Lake
- Cloud Systems
- Edge Computing
- Batch vs. Stream Processing
- Graph Database
- USING DATA
- Analysis vs. Analytics
- Descriptive Statistics
- Inferential Statistics
- Business Intelligence (BI)
- Artificial Intelligence (AI)
- Machine Learning (ML)
- Supervised Learning
- Regression Analysis
- Time Series Forecasting
- Unsupervised Learning
- Association Rules
- Reinforcement Learning
- Deep Learning
- Natural Language Processing (NLP)
- READING DATA
- Reading Data
- Data Quality Assessment
- Data Description
- Measures of Central Tendency
- Measures of Spread
- INTERPRETING DATA
- Data Interpretation
- Correlation Analysis
- Correlation Coefficient
- Correlation and Causation
- Simple Linear Regression
- Forecast Errors
- Statistical Tests
- Hypothesis Testing
- Statistical Significance
- Recall and Precision
Time remaining or 920 enrolls left
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