Introducing
APPLIED DATA SCIENCE

This skills-based specialization is intended for learners with a strong interest or who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular data science toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills in data science. Some of the contents that will be covered include Introduction to Data Science in Python, Applied Plotting, Data Analysis & Data Representation in Python, Applied Machine Learning and many other interesting ventures.
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Applied Data Science

Course Details

Background

Data scientist is one of the best suited professions to thrive this century. It is digital, programming-oriented, and analytical. Therefore, it comes as no surprise that the demand for data scientists has been surging in the job marketplace. Data science is a multidisciplinary field and It encompasses a wide range of topics such as Mathematics, Statistics, Programming, Data Visualization, Machine Learning and many other fields.  

Rationale

This skills-based specialization is intended for learners who have a basic python or programming background, and want to apply statistical, machine learning, information visualization, text analysis, and social network analysis techniques through popular python toolkits such as pandas, matplotlib, scikit-learn, nltk, and networkx to gain insight into their data. Some of the contents that will be covered include Introduction to Data Science in Python, Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning and many other interesting ventures. The course is ideal for beginners, as it starts from the fundamentals and gradually builds up your skills

1

Code

DSA111
2

Fees

K3,500 Per Person
Groups of more than 5 persons K3,000 per person

Payment Plan Available with Initial Deposit of K2000 to Enroll in the Course.
Balance to be paid within 2 weeks.
3

Location / Learning Mode

Online
4

Contact

Coordinator: Mr L Simukonda
Email: lsimukonda@mu.ac.zm
5

Dates

Intakes

Intake

Start Date

End Date

Time

 Group 1

 31st January 2022

 11th February 2022

 18 – 20

 Group 2

 14th February 2022

 25th February 2022

 18 – 20

 Group 3

 28th February 2022

 11th March 2022

 18 – 20

 Group 4

 14th March 2022

 25th March 2022

 18 – 20

 Group 5

 28th March 2022

 8th April 2022

 18 – 20

 Group 6

 11th April 2022

 22nd April 2022

 18 – 20

 

 

Group 6 to 10

Full Schedule To be Announced in April

 

Aim

The aim of this course is to provide learners with the necessary tools to analyse and interpret data for daily business decisions using data science techniques.

Objectives

At the end of the program Learners should be able to:

  1. Conduct an inferential statistical analysis
  2. Enhance a data analysis with applied machine learning
  3. Discern whether a data visualization is good or bad
  4. Analyze the connectivity of a social network

Competencies

  1. Statistical analysis, Python programming with NumPy, pandas, matplotlib
  2. Competence in pre-processing data
  3. Competence in cluster and factor analysis
  4. Use of Data Science Tool-Kits

Entry requirements

You will need a working computer and Microsoft Excel

Expected prior knowledge

Must have competency in using a computer and have knowledge of programming.

COURSE DELIVERY.

Intensive 2-3 weeks of lectures, hands-on practical and tutorials sessions.

QUALIFICATION

Upon successful completion, the candidates will be awarded a certificate in Applied Data Science and a grade appended to the certificate. This qualification will only apply to learners who pass the final exam and complete the assignments or quizzes.

Course Content

  1. Introduction to Python
  2. Advanced Statistical Methods In Python
  3. Mathematics
  4. Deep Learning
  5. Software Integration

Lesson Schedule

Day

Lesson/activity

Responsible/Lecturer

Day 1

Introduction to Python

Mr L Simukonda

Day 2

Advanced Statistical Methods In Python 1

Mr L Simukonda

Day 3

Advanced Statistical Methods In Python 2

Mr L Simukonda

Day 4

Mathematics

Mr L Simukonda

Day 5

Deep Learning

Mr L Simukonda

Day 6

Software Integration

Mr L Simukonda

Day 7 - 10

Data Analysis Tools (Tableau, D3.js,Jupyter,Weka)

Mr L Simukonda and Miss V Chama

Day 11-14

(Course might take a little bit longer if necessary) Personal Projects and Final Exam

Mr L Simukonda and Miss V Chama

Teaching Methods

  1. Lecture using virtual classrooms
  2. Practical hands-on online tutorials.
  3. Assessments using ICT technologies
  4. Zoom interactive software

Timing and schedules

Time: Most Lectures will be conducted in the evenings.
Tentatively from 18 hours to 20 hours

Assessment Method

  1. Assignment 30%
  2. Quizzes 10%
  3. Milestone project 60%

Certifications

Mulungushi University certificate will be provided

Connect with us

26 km from Kabwe Town along Great North Rd, Kabwe, Central Province, Zambia

  • dummy+(260) 215 228 004

  • dummy academic@mu.ac.zm

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