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Quick Facts

Medium Of Instructions	Mode Of Learning	Mode Of Delivery
English	Self Study	Video and Text Based

Course Overview

In a progressive statistics world, corporations want individuals who can extract key information from data in order to make smarter business decisions. UCT's Data Science with Python online course helps students build realistic data science and analytical abilities for implementing it in real-world business contexts.

According to a McKinsey report, organizations that have at least one senior leader who has been trained in data are 70% more likely to perform better. The Data Science and Python Training teaches about broadly applicable libraries of python and how these techniques may and are applied in real-world business settings. The course demonstrates the concept of uncovering more robust correlations in order to guarantee that training models are usable.

An NVP report states that 92% of C-suite leaders are expanding their expenditure on Artificial intelligence and Big data. The Data Science with Python syllabus provides statistical understanding, which serves as a basis for understanding the dynamics of artificial intelligence. The course delves into supervised learning algorithms via neural networks and tree-based models, as well as unsupervised learning algorithms of AI via hierarchical clustering and K-means.

The Highlights

Split option of payment
Shareable certificate
University of Cape Town offering
Downloadable resources
Projects and assessments
Course provider Getsmarter
Online learning
8 weeks duration
7-10 hours per week
Self-paced learning

Programme Offerings

Infographics
quizzes
Offline resources
video lectures
Self-paced learning
Case Studies
Live polls
online learning

Courses and Certificate Fees

Certificate Availability	Certificate Providing Authority
yes	UCT Cape Town

Fee type	Fee amount in INR
Data Science with Python fees	Rs. 45,564 (Incl of all taxes)

The Data Science with Python fee is Rs. 45,564 (Incl of all taxes). The total course fee must be paid at the time of admission or the candidate can choose the split method of payment in which the full payment is divided into 2 installments. If the candidate opts for the split method of payment, a 3% of additional admin fee will be added to the total fee. The payment can be made using a debit and credit card or EFT and bank transfer.

Eligibility Criteria

Certification Qualifying Details

To qualify for the Data Science with Python certification, Candidates will have to complete weekly modules and submit all the assignments and practicals included within the course. The candidate must take part in the live polls, quizzes, case studies, and class activities. Candidates must complete all the online practical assessments, as the evaluation is based on the series of online submitted practicals. The learner must meet all the prerequisites described in the course manual.

What you will learn

Data science knowledgeKnowledge of Python

After completing the Data Science with Python online training, Candidates will learn how to use data science and analytic approaches to make better decisions. Candidates will learn how to use various tools to create and modify accurate models that can be used to address business challenges. Learners will also gain a hands-on introduction to the extensively used Jupyter Notebook.

Who it is for

Data ScientistComputer Programmer

Professionals seeking to improve their analytical and data science knowledge and abilities can join the course.
IT workers in their early to mid-career who need to quickly upskill and expand their data science portfolio with demonstrated and practical abilities.
Professionals in a variety of business disciplines, such as sales, marketing, operations, and Finance, who want to study how to utilize data and computing to boost efficiency and uncover new possibilities for their company.

Admission Details

To get admission to the Data Science with Python course for beginners, follow the steps mentioned below:

Step 1. Go to the course website.

Step 2. Click on the ‘Register Now’ button to start the registration

Step 3. Read T&C carefully and agree to further proceed

Step 4. Generate a profile on the course provider Getsmarter website

Step 5. Fill in the billing details and provide sponsor details if applicable

Step 6. Pay the fee with a credit/debit card or bank transfer and start learning

The Syllabus

Discuss the value of data science in a particular domain
Recognize the fundamentals of data science
Identify supervised and unsupervised statistical learning problems
Determine the appropriate type of task for a given supervised problem
Determine the type of learning problem for a given situation
Justify statistical model selection using specified criteria

Summarise the algorithmic operations of tree-based methods to produce predictions.
Recognise the relationship between the responses and the feature space.
Execute Python code to train a tree-based model on a data set
Recommend a course of action based on information from a tree-based model
Analyze the predictions generated by a tree-based model

Discuss the importance of guarding against overfitting through pruning
Recognise the problem of overfitting in tree-based models
Apply pruning and validation methods to a tree-based model
Justify the need for pruning a tree-based model in a business context
Analyze the impact of pruning on tree-based models

Discuss the use of neural networks
Recognise the model mechanics of neural networks
Execute Python code to train a neural network on a data set
Defend the use of a neural network for a business problem
Analyze the predictions generated by a neural network model

Discuss the need for regularisation of neural networks
Recognise the use of regularisation in managing complexity in neural networks
Apply regularisation techniques to a neural network
Reflect on the impact of overfitted models in business applications
Compare a neural network before regularisation with one after regularisation

Explain the nuances of unsupervised learning
Recognise the model mechanics of K-means clustering algorithms
Practice clustering data using the K-means algorithm
Justify the use of the K-means algorithm on a given data set
Analyze the model output generated by the K-means algorithm

Explain the value of hierarchical clustering
Recognise a hierarchical clustering algorithm
Use Python to perform hierarchical clustering
Justify the use of a set of parameters and interpret the output
Analyze the output of a model using hierarchical clustering

Identify an appropriate model for a given data set
Recall the characteristics of explored statistical methods
Apply a statistical learning method to answer a business question
Assess a business problem and apply appropriate methods
Investigate the outcome of an applied method on a data set
Recognise how to download course materials and implement tools offline

Instructors

UCT Cape Town Frequently Asked Questions (FAQ's)

1: What is a data science course?

Data science is the study of using modern techniques and tools to discover meaningful information and unseen patterns for making better business decisions.

2: What does a data scientist do?

Data scientists assist companies in solving difficult challenges by sharing and extrapolating insights.

3: What is data science in simple words?

Data science is the study of extracting useful insights from data by combining subject experience, computer abilities, and understanding of statistics and mathematics.

4: What is python?

Python is an object-oriented, interpreted, and high-level programming language with a functional programming library concept.

5: What is python used for?

Python is a widely used general programming language that is used for software development and programming along with web development.