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Basic to Intermediate Course in Statistics for Data Scientists

Topic 1

Introduction and concepts
1 reading
Reading: What is data science and what do data scientists do?

Topic 2

Essential skills in Data science
4 readings
Reading: Statistics and statistical analysis
Reading: Statistical models/statistical learning
Reading: Machine learning
Reading: Difference between statistics and Machine Learning

Topic 3

Statistical concepts in the exploration of data sets
10 readings
Reading: Basic concepts in statistics
Reading: Frequency distribution tables for categorical variables
Reading: Frequency distribution tables for numeric variables
Reading: The Lorenz curve
Reading: Measures of central tendency
Reading: Measures of dispersion
Reading: Coefficient of variation
Reading: Measures of skewness
Reading: Five-number summary
Reading: Box plot

Topic 4

Basic probability concepts / Bayesian Theorem/decision trees
11 readings
Reading: Objective probability
Reading: Properties of probabilities – Union of 2 events
Reading: Properties of probabilities – Intersection of 2 events
Reading: Properties of probabilities – Mutually exclusive events
Reading: Properties of probabilities – Collectively exhaustive events
Reading: Properties of probabilities – Statistically independent events
Reading: Properties of probabilities – Marginal probability
Reading: Properties of probabilities – Joint probability
Reading: Properties of probabilities – Conditional probability
Reading: Properties of probabilities – Probability rules: addition and multiplication
Reading: Venn diagrams, Factorials, permutations, combinations

Topic 5

Probability distributions
4 readings
Reading: Poisson Distribution
Reading: Binomial distribution
Reading: Normal distribution and standard normal (CLT)
Reading: Student t – distribution

Topic 6

Confidence intervals
4 readings
Reading: One population: mean
Reading: Two populations: means
Reading: One population: proportion
Reading: Two populations: proportions

Topic 7

Hypothesis testing
2 readings
Reading: One population: means and proportions
Reading: Two populations: means and proportions

Topic 8

Hypothesis tests/cross tabulations
6 readings
Reading: Test for equality or proportions in 2 or more populations
Reading: Goodness-of-fit-test
Reading: Fitting the BD
Reading: Fitting the normal distribution
Reading: Fitting the PD
Reading: The rule of five

Topic 9

Analysis of Variance (ANOVA)
1 reading
Reading: Test hypothesis about multiple population means

Topic 10

Statistical models
3 readings
Reading: Linear regression
Reading: Correlation analysis
Reading: Testing the significance of the overall regression model

Topic 11

Introduction to Linear Algebra/Linear Equations

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Basic to Intermediate Course in Statistics for Data Scientists

R10 000
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About this course

Countdown till this short course launches!

Why Data Scientists need Statistical Skills?

More than ever before, organisations today are having to analyse vast amounts of data and are grappling with how they can make sense of the information hidden in the data to solve vexing problems in the world of business and government. For example, a business manager may want to know the characteristics of customers who are likely to buy the business’ products so that scarce resources can be targeted to this customer segment to maximize conversion rates and ultimately profits.

The data scientist uses statistical models, among other tools, to extract information from the data and generate actionable insights that can solve problems and achieve the organisation’s objectives.

Data scientists know that statistics is a powerful tool in the process of solving problems and making sound business decisions.

Using statistical methodologies and techniques such as data exploration, detailed analysis and modelling, the data scientist will gain a deeper understanding in to the structure of the data and provide management with high-level information to identify opportunities for business growth and solve problems.

To make sense of it all, a thorough understanding of the wonderful world of statistics is paramount.

Now you can see why this profession is in high demand!

Basic to Intermediary Course in Statistics for Data Scientists

Course Features

R 10 000

Launch

March 2021

Period of Course

15 weeks

Day

Every Saturday (except long weekends or holiday periods) from 09h00 to 13h00

Contact time

4 hours (9h00 – 13h00)

Hand-on Teaching by Expert Tutor (35 years of experience)

Notes handed out during class.

Extra lessons available

Certificate to be issued to successful candidates.

Frequently Asked Questions

How much is this course?

R 10 000 per attendee in full or in 2 instalments

What are examples of careers for data scientists?

Common careers in Data Science:

  • Data Scientist
  • Data Analyst
  • Business Intelligence Specialist
  • Data Architect
  • Business Analyst
  •  Business Information Technologist

What background knowledge do I need for this program?

This short course is open for anyone with any job and academic background.

Does the course material cover any computer science work?

The course does not include Computer Science, Machine Learning and Programming. Note, however, that Machine Learning is based on a Statistical and Mathematical Framework which is covered in the course.

What assignments need to be completed for this course?

  • Weekly assignments to be completed by attendees.
  • The assignments to count 30%, CAM.
  • Class Assignments are done in class.

Are extra lessons available for students?

Extra lessons, individual assistance to students attending regularly (minimum 80% of all lessons). Extra lessons to be held on Saturdays from 2 pm to 4 pm for students by appointment.

When is the final exam?

Final examination in week 16, three (3) hours (weight = 70%).

Syllabus

Topic 1 0/1

Introduction and concepts

1 hour

1 reading
Reading: What is data science and what do data scientists do?

Topic 2 0/4

Essential skills in Data science

1 hour

4 readings
Reading: Statistics and statistical analysis
Reading: Statistical models/statistical learning
Reading: Machine learning
Reading: Difference between statistics and Machine Learning

Topic 3 0/10

Statistical concepts in the exploration of data sets

12 hours

10 readings
Reading: Basic concepts in statistics
Reading: Frequency distribution tables for categorical variables
Reading: Frequency distribution tables for numeric variables
Reading: The Lorenz curve
Reading: Measures of central tendency
Reading: Measures of dispersion
Reading: Coefficient of variation
Reading: Measures of skewness
Reading: Five-number summary
Reading: Box plot

Topic 4 0/11

Basic probability concepts / Bayesian Theorem/decision trees

8 hours

11 readings
Reading: Objective probability
Reading: Properties of probabilities – Union of 2 events
Reading: Properties of probabilities – Intersection of 2 events
Reading: Properties of probabilities – Mutually exclusive events
Reading: Properties of probabilities – Collectively exhaustive events
Reading: Properties of probabilities – Statistically independent events
Reading: Properties of probabilities – Marginal probability
Reading: Properties of probabilities – Joint probability
Reading: Properties of probabilities – Conditional probability
Reading: Properties of probabilities – Probability rules: addition and multiplication
Reading: Venn diagrams, Factorials, permutations, combinations

Topic 5 0/4

Probability distributions

6 hours

4 readings
Reading: Poisson Distribution
Reading: Binomial distribution
Reading: Normal distribution and standard normal (CLT)
Reading: Student t – distribution

Topic 6 0/4

Confidence intervals

4 hours

4 readings
Reading: One population: mean
Reading: Two populations: means
Reading: One population: proportion
Reading: Two populations: proportions

Topic 7 0/2

Hypothesis testing

6 hours

2 readings
Reading: One population: means and proportions
Reading: Two populations: means and proportions

Topic 8 0/6

Hypothesis tests/cross tabulations

6 hours

6 readings
Reading: Test for equality or proportions in 2 or more populations
Reading: Goodness-of-fit-test
Reading: Fitting the BD
Reading: Fitting the normal distribution
Reading: Fitting the PD
Reading: The rule of five

Topic 9 0/1

Analysis of Variance (ANOVA)

4 hours

1 reading
Reading: Test hypothesis about multiple population means

Topic 10 0/3

Statistical models

6 hours

3 readings
Reading: Linear regression
Reading: Correlation analysis
Reading: Testing the significance of the overall regression model

Topic 11

Introduction to Linear Algebra/Linear Equations

6 hours

Reviews

Our course begins with the first step for generating great user experiences: understanding what people do, think, say, and feel. In this module, you’ll learn how to keep an open mind while learning.

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