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Read, Understand, and Analyze Data

What you will 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

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:

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


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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 β€˜Buy Now’ button and let’s start this exciting journey today!

English
language

Content

Introduction

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
Database
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
Classification
Unsupervised Learning
Clustering
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
R-Squared
Forecasting
Forecast Errors
Statistical Tests
Hypothesis Testing
P-Value
Statistical Significance
Classification
Accuracy
Recall and Precision