English | MP4 | AVC 1280×720 | AAC 44KHz 2ch | 58 lectures (4h 12m) | 984 MB
Read, Understand, and Analyze Data
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 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
Table of Contents
Introduction
1 What does the course cover What is Data Literacy
2 Why do we Need Data Literacy
3 Data-driven Decision Making
4 Benefits of Data Literacy
5 How to Get Started
UNDERSTANDING DATA
6 Data Definition
7 Qualitative vs. Quantitative Data
8 Structured vs. Unstructured Data
9 Data at Rest vs. Data in Motion
10 Transactional vs. Master Data
11 Big Data
12 Storing Data
13 Database
14 Data Warehouse
15 Data Marts
16 The ETL Process
17 Apache Hadoop
18 Data Lake
19 Cloud Systems
20 Edge Computing
21 Batch vs. Stream Processing
22 Graph Database
USING DATA
23 Analysis vs. Analytics
24 Descriptive Statistics
25 Inferential Statistics
26 Business Intelligence (BI)
27 Artificial Intelligence (AI)
28 Machine Learning (ML)
29 Supervised Learning
30 Regression Analysis
31 Time Series Forecasting
32 Classification
33 Unsupervised Learning
34 Clustering
35 Association Rules
36 Reinforcement Learning
37 Deep Learning
38 Natural Language Processing (NLP)
READING DATA
39 Reading Data
40 Data Quality Assessment
41 Data Description
42 Measures of Central Tendency
43 Measures of Spread
INTERPRETING DATA
44 Data Interpretation
45 Correlation Analysis
46 Correlation Coefficient
47 Correlation and Causation
48 Simple Linear Regression
49 R-Squared
50 Forecasting
51 Forecast Errors
52 Statistical Tests
53 Hypothesis Testing
54 P-Value
55 Statistical Significance
56 Classification
57 Accuracy
58 Recall and Precision
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