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Course / Course Details

Data Science and Analytics Fundamentals

  • Super admin image

    By - Super admin

  • 20 students
  • 9 Hours 14 Min
  • (0)

Course Requirements

Anyone can learn and become a data analytics professional. However, here are some prerequisites that would be helpful for choosing a career in data analytics: Ability to work with numbers and quantitative stuff Some programming experience Willingness to learn statistical concepts Passion for solving problems

Basic computer literacy

Access to a laptop or PC with internet connectivity

A willingness to learn and think critically with data

If you plan to switch to being a data analyst but bear no experience in the industry, this data analytics tutorial will provide you with a strong foundation in the subject.

Course Description

Welcome to this course. Data is every detail of our very existence. Conciously or not, we all contribute to global data and this data is relevant to different oganisations, in different forms to aid important decision making processess. Data analytics is the science of analyzing data and extracting meaningful information that can be used to make better decisions in an organization. This is something we all start doing from an early age. Analytics will help demystify data so you can use it to make informed decisions, recognize trends, detect outliers, and summarize data sets to inform business decisions. Everything we do involves systems and processes. We buy and sell things; we eat, we travel, we watch TV, we surf the internet, we listen to music. With all this activity taking place, global businesses are drowning in data and they increasingly rely on people with good analytics skills to inform organizational decision-making. Overtime, the importance of data analytics have risen and this has led to the demand for analytical skills. It’s the data analyst’s job to translate the data into actionable — and profitable — business intelligence. Go above and beyond your peers by gaining the skill packed in this course.

Course Outcomes

By the end of this course learners will be able to

Understand the role and value of data science and analytics in modern industries

Define and describe the data analysis lifecycle

Identify common data types and sources

Apply basic statistical concepts such as mean median mode and standard deviation

Create and interpret simple data visualizations like charts graphs tables

Recognize the difference between descriptive predictive and prescriptive analytics

Use analytical thinking to approach real world problems

Prepare for more advanced learning in data science analytics or related fields

Course Curriculum

  • 9 chapters
  • 44 lectures
  • 9 quizzes
  • 9 Hours 14 Min total length
Toggle all chapters
1 Data Analytics Fundamentals
4 Min

Module 1 Lesson 1


2 Guide to Using Anaconda for Python Data Analytics
5 Min

Module 1 Summary


3 Walkthrough 1 Step by Step Guide to Using Anaconda for Python Data Analytics
14 Min


4 Quiz 1 DAF [Quiz]
10 Min


5 DAF Exercise 1
1 Hour

Module 1


1 Data Collection
5 Min

Module 2 Lesson 1


2 Walkthrough 2 Data Collection and Cleaning
14 Min


3 Quiz 2 DAF [Quiz]
10 Min


4 DAF Exercise 2
1 Hour


1 Exploratory Data Analysis
5 Min

Module 3 Lesson 1


2 Data Correlation
3 Min

Module 3 Lesson 2


3 Walkthrough 3 Exploring and Visualizing Customer Data
14 Min


4 DAF Exercise 3
1 Hour

Module 3


5 Quiz 3 DAF [Quiz]
10 Min


1 Social Media Analytics
4 Min

Module 4 Lesson 1


2 Sentiments Analysis
4 Min

Module 4 Lesson 2


3 Tools For Analysis
4 Min

Module 4 Lesson 3


4 Walkthrough 4 Analyzing Social Media Posts
14 Min


5 Quiz 4 DAF [Quiz]
10 Min


6 DAF Exercise 4
2 Hours

Module 4


1 Transport Analysis
5 Min

Module 5 Lesson 1


2 Route Algorithms
6 Min

Module 5 Lesson 2


3 Walkthrough 5 Datasets
10 Min


4 DAF Exercise 5
1 Hour 30 Min

Module 5


5 Quiz 5 DAF [Quiz]
10 Min


1 Inventory Analysis
4 Min

Module 6 Lesson 1


2 Key Metrics
4 Min

Module 6 Lesson 2


3 Walkthrough 6 Inventory Analytics with Python
8 Min


4 Quiz 6 DAF [Quiz]
10 Min


5 DAF Exercise 6
1 Hour

Module 6


1 Survey Analysis
4 Min

Module 7 Lesson 1


2 Python Tools
4 Min

Module 7 Lesson 2


3 Analyzing Customer Satisfaction Survey Data
9 Min


4 DAF Exercise 7
1 Hour 30 Min

Module 7


5 Quiz 7 DAF [Quiz]
10 Min


1 Data Science and Machine Learning
4 Min

Module 8 Lesson 1


2 Types of Machine Learning
5 Min

Module 8 Lesson 2


3 Role of Python in Data Science and Machine Learning
6 Min

Module 8 Lesson 3


4 Measures of Dispersion
5 Min

Module 8 Lesson 4


5 Walkthrough 8 Exploring the Iris Dataset
10 Min


6 Quiz 8 DAF [Quiz]
10 Min


7 DAF Exercise 8
1 Hour

Module 8


1 Final Exam DAF [Quiz]
40 Min


2 DAF Course Capstone Project
2 Hours


1. Step by Step Guide to Using Anaconda for Python Data Analytics
2. Retail sales data file
3. Customers raw excel sheet
4. Customer survey responses sheet
5. Module3 customer data
6. Your Dataset
7. Transportation analytics dataset
8. Survey responses module7
9. Module7 survey data
10. Inventory sample dataset
11. Iris Dataset
12. Module 2 Walkthrough file
13. Module 3 Walkthrough file
14. Module 4 Exercise Dataset
15. Module 4 Walkthrough file
16. Module 5 Walkthrough file
17. Module 6 Walkthrough file
18. Module 7 Walkthrough file
19. Module 8 Walkthrough file

Instructor

Super admin

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