Data Literacy

with Olivier Maugain

4.8/5

(996)

In this course, we will talk about the importance of data literacy. Understanding the language of data allows you to use and interpret data effectively. If you want a successful career path, you will most likely need these skills.

4 hours 58 lessons

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Course Overview

Data literacy involves articulating a problem that can potentially be solved using data. Most importantly, it is about interpreting the results of an analysis and making decisions based on the gained insights. A data literate person would have the ability to understand and check the adequacy of the data involved. Ultimately, you should be able to read and derive valuable information from data. We will cover all these topics extensively in the course.

58 High Quality Lessons

4 Practical Tasks

4 Hours of Video

Certificate of Achievement

Topics covered

Business analyticsdata analysismachine learningTheory

What You'll Learn

Being data literate means having the necessary competencies to work with data. Someone who is data literate would have the ability to:

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

Articulate a problem that can potentially be solved using data

Curriculum

Introduction to Data Literacy Free

5 Lessons 24 Min

In this section, you will learn what data literacy is and why business professionals need it. We will examine the benefits of data literacy and data-driven decision making.
What Exactly is Data Literacy Free Why do We Need Data Literacy Free Data-driven Decision Making Free Benefits of Data Literacy Free How to Get Started Free
Understanding Data

17 Lessons 62 Min

Section 2 is about understanding data terminology – we will explain the different types of data, data storage systems, and the technical tools required to analyze data.
Data Definition Free Qualitative vs. Quantitative Data Free Structured vs. Unstructured Data Free Data at Rest vs. Data in Motion Free 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

16 Lessons 71 Min

In this section, we will show you how to use data. We’ll talk about business intelligence (BI), artificial intelligence (AI), as well as various machine and deep learning techniques.
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 Analysis Association Rules Reinforcement Learning Deep Learning Natural Language Processing (NLP)
Reading Data

5 Lessons 30 Min

In the fourth chapter of the course, you will find out how to comprehend data, perform data quality assessments, and read major statistics (measures of central tendency and measures of spread).
Reading Data Data Quality Assessment Data Description Measures of Central Tendency Measures of Spread
Interpreting Data

15 Lessons 65 Min

The last section focuses on data interpretation. You will examine fundamental analysis techniques such as correlation, simple linear regression (r-squared and p-values), forecasting, statistical tests, and many more.
Interpreting Data Correlation Аnalysis Correlation Coefficient Correlation and Causation Simple Linear Regression R-squared Forecasting Forecast Errors Statistical Tests Hypothesis Testing P-Value Statistical Significance Classification Models Accuracy Recall and Precision

Student feedback

4.8/5

996 ratings

5 stars

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4 stars

147 (15%)

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2 stars

5 (1%)

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2 (0%)

Moki Valantine

20.11.2022

Yh, your courses are explicitly great such hard work is being put in here. I tremendously appreciate them. But as a level 4 course reader now I have some recommendations that could better ease our understanding of the course materials. 1. I understand that animation presentations of lectures are a faster way and quite a good teaching method.. but when I did the Python Bootcamp it was quite better because I felt like someone was talking to me. Hence, I will appreciate it if the tutors showcase themselves as we move through the courses. 2. Although we have practical examples I will love if real-life projects and videos of how workers in the company discuss and solve them, they should be shown or even as a data major intern goes to work and his job descriptions at the office this help us to prepare for the working environment. More projects and problems faced by companies in each course. Thanks. 3. Take us out of the learning environment sometimes surveying companies and showing us how the data is collected.

Dhaivat Parekh

05.11.2022

Data types like Nominal, Ordinal, Interval, Ratio should have better elaboration and examples. There should be relevant tests/questions interspersed between modules so that the student feels confident that s/he is able to understand correctly

Vijay Saradhi Reddy Sakati

11.11.2022

Whatever I write here will not be good enough for this course, the way the concepts are presented in a sequential order and clearly explaining about each is Very Exceptional. Thank you Sir, looking forward to more courses from you.

Areeb Ansari

18.12.2022

The resources could have been more elaborative containing the minute/subparts of the content, especially for the statistical terms, i.e. its tough to keep the statistical terminologies intact in the head without lecture notes.

Eve Lee

17.07.2022

Data literacy is almost like a recap to data strategy and lays the foundation for statistics later on. I do wish the class had captions, but I think it turned out alright in the end. Thanks for the great content!