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Description

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:
Articulate a problem that can potentially be solved using data
Understand the data sources involved
Check the adequacy and fitness of data involved
Interpret the results of an analysis and extract insights
Make decisions based on the insights
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 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

Requirements

  • You will need a copy of Adobe XD 2019 or above. A free trial can be downloaded from Adobe.
  • No previous design experience is needed.
  • No previous Adobe XD skills are needed.

Course Content

27 sections • 95 lectures
Expand All Sections
1-Introduction
9
1.1-What does the course cover? What is Data Literacy?
1.2-Why do we Need Data Literacy?
1.3-Why do we need data literacy?
1.4-Data-driven Decision Making
1.5-Data-driven Decision Making
1.6-Benefits of Data Literacy
1.7-Benefits of Data Literacy
1.8-How to Get Started?
1.9-How to Get Started?
2-UNDERSTANDING DATA
33
2.1-Data Definition
2.2-Data Definition
2.3-Qualitative vs. Quantitative Data
2.4-Qualitative vs. Quantitative Data
2.5-Structured vs. Unstructured Data
2.6-Structured vs. Unstructured Data
2.7-Data at Rest vs. Data in Motion
2.8-Data at Rest vs. Data in Motion
2.9-Transactional vs. Master Data
2.10-Transactional vs. Master Data
2.11-Big Data
2.12-Big Data
2.13-Storing Data
2.14-Database
2.15-Database
2.16-Data Warehouse
2.17-Data Warehouse
2.18-Data Marts
2.19-Data Marts
2.20-The ETL Process
2.21-The ETL Process
2.22-Apache Hadoop
2.23-Apache Hadoop
2.24-Data Lake
2.25-Data Lake
2.26-Cloud Systems
2.27-Cloud Systems
2.28-Edge Computing
2.29-Edge Computing
2.30-Batch vs. Stream Processing
2.31-Batch vs. Stream Processing
2.32-Graph Database
2.33-Graph Database
3-USING DATA
32
3.1-Analysis vs. Analytics
3.2-Analysis vs. Analytics
3.3-Descriptive Statistics
3.4-Descriptive Statistics
3.5-Inferential Statistics
3.6-Inferential Statistics
3.7-Business Intelligence (BI)
3.8-Business Intelligence (BI)
3.9-Artificial Intelligence (AI)
3.10-Artificial Intelligence (AI)
3.11-Machine Learning (ML)
3.12-Machine Learning (ML)
3.13-Supervised Learning
3.14-Supervised Learning
3.15-Regression Analysis
3.16-Regression Analysis
3.17-Time Series Forecasting
3.18-Time Series Forecasting
3.19-Classification
3.20-Classification
3.21-Unsupervised Learning
3.22-Unsupervised Learning
3.23-Clustering
3.24-Clustering
3.25-Association Rules
3.26-Association Rules
3.27-Reinforcement Learning
3.28-Reinforcement Learning
3.29-Deep Learning
3.30-Deep Learning
3.31-Natural Language Processing (NLP)
3.32-Natural Language Processing (NLP)
4-READING DATA
10
4.1-Reading Data
4.2-Reading Data
4.3-Data Quality Assessment
4.4-Data Quality Assessment
4.5-Data Description
4.6-Data Description
4.7-Measures of Central Tendency
4.8-Measures of Central Tendency
4.9-Measures of Spread
4.10-Measures of Spread
5-INTERPRETING DATA
29
5.1-Data Interpretation
5.2-Data Interpretation
5.3-Correlation Analysis
5.4-Correlation Analysis
5.5-Correlation Coefficient
5.6-Correlation Coefficient
5.7-Correlation and Causation
5.8-Correlation and Causation
5.9-Simple Linear Regression
5.10-Simple Linear Regression
5.11-R-Squared
5.12-R-Squared
5.13-Forecasting
5.14-Forecasting
5.15-Forecast Errors
5.16-Forecast Errors
5.17-Statistical Tests
5.18-Hypothesis Testing
5.19-Hypothesis Testing
5.20-P-Value
5.21-P-Value
5.22-Statistical Significance
5.23-Classification
5.24-Classification
5.25-Accuracy
5.26-Accuracy
5.27-Recall and Precision
5.28-Recall and Precision
5.29-Bonus Lecture