Get updates on Eligibility, Admission, Placements Fees Structure
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. 52,218 |
Installments plan
| 1st installment | Required before: 2025-01-21 | Amount Due: ₹26,109.00 INR |
| 2nd installment | Required before: 2025-02-20 | Amount Due: ₹26,109.00 INR |
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
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
- 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
- Recognise the fundamentals of data science
- Discuss the value of data science in a particular domain
- Identify supervised and unsupervised statistical learning problems
- Determine the type of learning problem for a given situation
- Determine the appropriate type of task for a given supervised problem
- Justify statistical model selection using specified criteria
- Recognise the relationship between the responses and the feature space.
- Summarise the algorithmic operations of tree-based methods to produce predictions.
- Execute Python code to train a tree-based model on a data set
- Analyse the predictions generated by a tree-based model
- Recommend a course of action based on information from a tree-based model
- Recognise the problem of overfitting in tree-based models
- Discuss the importance of guarding against overfitting through pruning
- Apply pruning and validation methods to a tree-based model
- Analyse the impact of pruning on tree-based models
- Justify the need for pruning a tree-based model in a business context
- Recognise the model mechanics of neural networks
- Discuss the use of neural networks
- Execute Python code to train a neural network on a data set
- Analyse the predictions generated by a neural network model
- Defend the use of a neural network for a business problem
- Recognise the use of regularisation in managing complexity in neural networks
- Discuss the need for regularisation of neural networks
- Apply regularisation techniques to a neural network
- Compare a neural network before regularisation with one after regularisation
- Reflect on the impact of overfitted models in business applications
- Recognise the model mechanics of K-means clustering algorithms
- Explain the nuances of unsupervised learning
- Practise clustering data using the K-means algorithm
- Analyse the model output generated by the K-means algorithm
- Justify the use of the K-means algorithm on a given data set
- Recognise a hierarchical clustering algorithm
- Explain the value of hierarchical clustering
- Use Python to perform hierarchical clustering
- Analyse the output of a model using hierarchical clustering
- Justify the use of a set of parameters and interpret the output
- Recall the characteristics of explored statistical methods
- Identify an appropriate model for a given data set
- Apply a statistical learning method to answer a business question
- Investigate the outcome of an applied method on a data set
- Assess a business problem and apply appropriate methods
- Recognise how to download course materials and implement tools offline
Instructors
UCT Cape Town Frequently Asked Questions (FAQ's)
Data science is the study of using modern techniques and tools to discover meaningful information and unseen patterns for making better business decisions.
Data scientists assist companies in solving difficult challenges by sharing and extrapolating insights.
Data science is the study of extracting useful insights from data by combining subject experience, computer abilities, and understanding of statistics and mathematics.
Python is an object-oriented, interpreted, and high-level programming language with a functional programming library concept.
Python is a widely used general programming language that is used for software development and programming along with web development.