Data Science With Python

Course ID
DSP
Department
Software Engineering
Campus
1 Cornhill
Level
Certificate
Method
Lecture, Project
Duration
3 Months
Data Science with Python 2
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Optional Add-on Programs

Job Guarantee Program

The Job Guarantee program is available only to candidates who enroll in Option 6 (Project and Industrial Training and Paid Internship Program + Pro Plan). It is important to note, however, that the Job Guarantee program has its own selection criteria, so not everyone may be considered for the program. To learn more about the Job Guarantee program, please visit Job Guaranteed Software Courses

Pro Plan Card

LSET PRO PLAN

Are you eager to enter the workforce fully prepared? Look no further than our LSET PRO PLAN! This is an add-on program that you can select during your course enrolment, it offers a personalised learning experience that helps you succeed in your course, build your technical portfolio, and advance your professional journey.
Curious about how to embark on this journey? Simply “click” here to learn more and kickstart your professional development with us!

This course will guide you in learning how to utilise the strength of Python in data analysis, creating appealing visualisations, and using robust machine learning algorithms. This course is designed for both beginners with basic programming experience and experienced developers who are willing to jump into Data Science.

Apply now to become a professional Data Science with Python

Are you looking for corporate training?
We tailor our courses to meet the specific needs of your team. If you would like to discuss your training requirements, please email [email protected] today.
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Course feature icons

Prerequisites have been met

Options
Topic
Add-On
Duration
Options
Option 1
Topic
Data Science with Python (Prior Knowledge of Python Required)
Add-On
Duration
3 Months
Options
Option 2
Topic
Data Science with Python (Prior Knowledge of Python Required)
Add-On
Project (Online)
Duration
5 Months
Options
Option 3
Topic
Data Science with Python (Prior Knowledge of Python Required)
Add-On
Project (Online) &
  Industrial Training and Paid Internship Program (Remote)
Duration
12 Months

Prerequisites have not been met

Options
Topics
Add-On
Duration
Options
Option 1
Topics
Python + Data Science with Python
Add-On
Duration
4 Months
Options
Option 2
Topics
Python + Data Science with Python
Add-On
Project (Online)
Duration
6 Months
Options
Option 3
Topics
Python + Data Science with Python
Add-On
Project (Online) &
  Industrial Training and Paid Internship Program (Remote)
Duration
13 Months

Tuition Fees

Options
Course Pack
Home (UK) & International
Online
Home (UK)
Classroom
International
Classroom
Options
Option 1
Course Pack
Data Science with Python (Prior Knowledge of Python Required) – (3 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £1,860
Pay Per Module: 
Number of Module: 2
Per Module Fee: £1,116
Home Classroom
Pay Upfront (with 20% Disc) : £3,660
Pay Per Module:
Number of Module: 2
Per Module Fee: £2,196
International Classroom
£6,060
Options
Option 2
Course Pack
Data Science with Python (Prior Knowledge of Python Required) + Project (Online) – (5 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £3,060
Pay Per Module: 
Number of Module: 3
Per Module Fee: £1,224
Home Classroom
Pay Upfront (with 20% Disc) : £6,060
Pay Per Module: 
Number of Module: 3
Per Module Fee: £2,424
International Classroom
£8,460
Options
Option 3
Course Pack
Data Science with Python (Prior Knowledge of Python Required) + Project (Online) + Industrial Training and Paid Internship Program (Remote) – (12 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £6,060
Pay Per Module:
Number of Module: 10
Per Module Fee: £728
Home Classroom
Pay Upfront (with 20% Disc) : £13,260
Pay Per Module: 
Number of Module: 10
Per Module Fee: £1,592
International Classroom
£15,660
Options
Option 4
Course Pack
Data Science with Python (Prior Knowledge of Python Required) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £2,340
Pay Per Module:
Number of Module: 2
Per Module Fee: £1,404
Home Classroom
Pay Upfront (with 20% Disc) : £4,140
Pay Per Module:
Number of Module: 2
Per Module Fee: £2,484
International Classroom
£6,540
Options
Option 5
Course Pack
Data Science with Python (Prior Knowledge of Python Required) + Project (Online) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £3,540
Pay Per Module:
Number of Module: 3
Per Module Fee: £1,416
Home Classroom
Pay Upfront (with 20% Disc) : £6,540
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,616
International Classroom
£8,940
Options
Option 6
Course Pack
Data Science with Python (Prior Knowledge of Python Required) + Project (Online) + Industrial Training and Paid Internship Program (Remote) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £6,540
Pay Per Module:
Number of Module: 10
Per Module Fee: £785
Home Classroom
Pay Upfront (with 20% Disc) : £13,740
Pay Per Module:
Number of Module: 10
Per Module Fee: £1,649
International Classroom
£16,140

Prerequisites have not been met

Options
Course Pack
Home (UK) & International
Online
Home (UK)
Classroom
International
Classroom
Options
Option 1
Course Pack
Python + Data Science with Python – (4 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £2,700
Pay Per Module: 
Number of Module: 3
Per Module Fee: £1,080
Home Classroom
Pay Upfront (with 20% Disc) : £5,100
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,040
International Classroom
£9,000
Options
Option 2
Course Pack
Python + Data Science with Python + Project (Online) – (6 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £3,900
Pay Per Module: 
Number of Module: 5
Per Module Fee: £936
Home Classroom
Pay Upfront (with 20% Disc) : £7,500
Pay Per Module: 
Number of Module: 5
Per Module Fee: £1,800
International Classroom
£11,880
Options
Option 3
Course Pack
Python + Data Science with Python + Project (Online) + Industrial Training and Paid Internship Program (Remote) – (13 Months)
Home & International Online
Pay Upfront (with 20% Disc) : £6,900
Pay Per Module:
Number of Module: 10
Per Module Fee: £828
Home Classroom
Pay Upfront (with 20% Disc) : £14,700
Pay Per Module: 
Number of Module: 10
Per Module Fee: £1,764
International Classroom
£20,520
Options
Option 4
Course Pack
Python + Data Science with Python + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £3,180
Pay Per Module:
Number of Module: 3
Per Module Fee: £1,272
Home Classroom
Pay Upfront (with 20% Disc) : £5,580
Pay Per Module:
Number of Module: 3
Per Module Fee: £2,232
International Classroom
£9,576
Options
Option 5
Course Pack
Python + Data Science with Python + Project (Online) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £4,380
Pay Per Module:
Number of Module: 5
Per Module Fee: £1,052
Home Classroom
Pay Upfront (with 20% Disc) : £7,980
Pay Per Module:
Number of Module: 5
Per Module Fee: £1,916
International Classroom
£12,456
Options
Option 6
Course Pack
Python + Data Science with Python + Project (Online) + Industrial Training and Paid Internship Program (Remote) + Pro Plan
Home & International Online
Pay Upfront (with 20% Disc) : £7,380
Pay Per Module:
Number of Module: 10
Per Module Fee: £886
Home Classroom
Pay Upfront (with 20% Disc) : £15,180
Pay Per Module:
Number of Module: 10
Per Module Fee: £1,822
International Classroom
£21,096
   Note: Our Industrial Training and Internship program includes a guaranteed 6 months paid internship (from 10 hours to 40 hours per week) with a technology company. Due to visa restrictions, some international students may not be able to participate in this program.

Data Scientist is one of the most amazing career options that offer high salaries, immense job satisfaction, and astonishing growth opportunities. Further, this profession offers an amazing job satisfaction rating of 4.4 out of 5. As per the survey by Harvard Business Review, Data Scientist is the most desirable profession of the 21st century. In this course, we will work on your core skills like a python programming language, frameworks for data science processes, Machine learning, and data science projects, hands-on practice of data preprocessing techniques, and a lot more.

Career perspective

Data science is one of the most lucrative and desirable career options for It Professionals. As per the survey growth for data science, jobs will grow about 30% through 2026. Currently, the average salary of a Data Scientist in the UK is £49,591 annually. Data scientists are hot assets currently. A career in this domain promises insanely high salary, Global recognition, and amazing growth opportunities.

Data science is everywhere, be it smart cars, automated voice assistants, smart factories, smart sales predictions, smart investment, smart marketing, and advertising, smart decision making, and many more. Companies across the globe are rushing towards automation which manifests that there is a huge demand for data science professionals. Top companies like Facebook, Apple, Google, Oracle, Microsoft, IBM, Amazon, and many more hiring data science professionals.

Technologies Covered

Python programming language: It is an interpreted high-level programming language used for web development, machine learning, AI, ML, and a lot more. It provides a clear approach to programmers to write a clear and logical approach.

NumPy: it is a python library that consists of the multidimensional array and a collection of mathematical functions to operate on this array

Pandas is a software library used for Data Analysis and manipulation. It offers operations and data structure and operations for manipulating time series and numerical tables.

Matplotlib is a python library that makes matplotlib work like MATLAB. It provides an object-oriented API for inculcating plots into the application using GUI.

Plotly is an open-source plotting library that supports a wide range of scientific, financial, and geographical use cases.

SciKit-Learn is a Machine Learning library for the Python programming language. It features various regression, classification, and clustering algorithms.

Complementary Workshops

Git Management

Agile Project Management

Agile Project Management

Team Building

Personality Development

Interview Preparation

Course Information

Course Intakes

September

End: December

January

End: April

May

End: August

Entry Criteria

  • Some prior programming experience
  • Ability to work in a team
  • If a potential student’s first language is not English, they must also reach the English Language requirements of either any one of the following - IELTS 5.5 or NCC Test or GCE “O” Level English C6.
  • Have access to personal laptop

Course Highlights

  • Hands-on practice
  • Industry-standard Project development
  • Learn from experts
  • Interactive teaching
  • Personality development

Evaluation Criteria

  • 18 Coding exercises
  • 5 Assignments
  • 5 Quizzes
  • Capstone Project
  • Group activities
  • Presentations

Learning Objectives

  • Use Python for Data Science and Machine Learning
  • Use Spark for Big Data Analysis
  • Implement Machine Learning Algorithms
  • Learn to use NumPy for Numerical Data
  • Learn to use Pandas for Data Analysis
  • Learn to use Matplotlib for Python Plotting
  • Learn to use Seaborn for statistical plots
  • Use Plotly for interactive dynamic visualisations
  • Use SciKit-Learn for Machine Learning Tasks
  • K-Means Clustering
  • Logistic Regression
  • Linear Regression
  • Random Forest and Decision Trees
  • Natural Language Processing and Spam Filters
  • Neural Networks
  • Support Vector Machines

Weekday Batches

Batch 01
09:00 am – 11:00 am
(Tue, Thu)

Batch 02
12:00 pm – 02:00 pm
(Tue, Thu)

Batch 03
03:00 pm – 05:00 pm
(Tue, Thu)

Batch 04
05:30 pm – 07:30 pm
(Tue, Thu)

Weekend Batches

Batch 01
08:00 am – 10:00 am
(Sat, Sun)

Batch 02
10:00 am – 12:00 pm
(Sat, Sun)

Hands-on Workshops

Interview Preparation

CV Preparation

Personality Development

Join Now

Enroll now and get an amazing deal with career guidance. Get in touch with our counsellors shortly.

Apply Now

Course Content

Browse the LSET interactive and practical curriculum

Introduction

>> Course Introduction >> Installation of prerequisites

Python crash course

>> Introduction to Python >> Python course part 1
>> Python course part 2 >> Python Overview

Data science with Python- NumPy

>> Introduction to Numpy >> Numpy operations
>> Numpy Array >> Numpy operations
>> Numpy Indexing >> Numpy overview

Data science with Python- Pandas

>> Introduction to Pandas >> DataFrames >> Merging Joining and Concatenating
>> Pandas Operations >> Data Functions

Data science with python- Matplotlib

>> Introduction >> Matplotlib part 1 >> Matplotlib part 2 >> Overview

Data science with python- Seaborn

>> Introduction to Seaborn >> Categorical Plots >> Matrix Plots >> Regression Plots
>> Practice exercise >> Solutions

Introduction to machine learning

>> Overview of machine learning >> Linear Regression >> K Nearest Neighbors
>> K Means Clustering >> Decision Trees >> Random Forests
>> Natural Language Processing >> Neural Nets

Deep learning

>> Download and Install TensorFlow >> Introduction to ANN >> Introduction to Tensorflow >> Tensorflow basic syntax
>> Tensorflow regression code >> Tensorflow classification code >> Keras Overview

*Modules of our curriculum are subject to change. We update our curriculum based on the new releases of the libraries, frameworks, Software, etc. Students will be informed about the final curriculum in the course induction class.

Having Doubts?

Contact LSET Counsellor

We love to answer questions, empower students, and motivate professionals. Feel free to fill out the form and clear up your doubts related to our Data Science with Python Course

Best Career Paths

Data Analyst

A data analyst collects, cleans, and interprets data sets in order to answer a question or solve a problem. They can work in many industries, including business, finance, criminal justice, science, medicine, and government.

Data Engineers

Data engineers work in a variety of settings to build systems that collect, manage, and convert raw data into usable information for data scientists and business analysts to interpret. Their ultimate goal is to make data accessible so that organisations can use it to evaluate and optimise their performance.

Database Administrator

Database administrators (DBAs) work with technology, using specialised types of software to store and organise a company's data. This could include a variety of information, from confidential financial numbers, to payroll data, to customer shipping records.

Machine Learning Engineer

A machine learning engineer (ML engineer) is a person in IT who focuses on researching, building and designing self-running artificial intelligence (AI) systems to automate predictive models.

Data Scientist

A data scientist is someone who makes value out of data. Such a person proactively fetches information from various sources and analyses it for better understanding about how the business performs, and to build AI tools that automate certain processes within the company.

Data Architect

Data architects are senior visionaries who translate business requirements into technology requirements and define data standards and principles. The data architect is responsible for visualising and designing an organisation's enterprise data management framework.

Top Companies Hiring

IBM

IBM

WIPRO

Wipro

Cloudera

Splunk

Numerator

Faculties & Mentors

Mayur Ramgir

Mayur Ramgir

Mentor Panel

Rolando Carrasco

Rolando Carrasco

Who should apply for this course?

  • Students who are willing to take data science as a career and want proficiency in this domain.
  • Students who want to achieve a great and successful career.
  • It is the best course for students who want to build a strong foundation in data science.
  • It is also a great course for working professionals who want to shift to the data science domain.

Reasons to learn Data science

  • Amazing job opportunities as the demand for data science professionals is increasing at a high pace
  • As per the survey average salary of data science, professionals is £52,052.
  • Data science enhances your decision-making power and it is easy to learn and understand.
  • Less completion in the field as there is an alarming gap between the demand and supply of data science professionals.

About the Course

This course will guide you in learning how to utilise the strength of Python in data analysis, creating appealing visualisations, and using robust machine learning algorithms. This course is designed for both beginners with basic programming experience and experienced developers who are willing to jump into Data Science.

You will learn how to use NumPy, Seaborn, Pandas, Matplotlib, Python overview, Machine Learning concepts, and more. Further, you will get a chance to enhance your learning skills through Hands-on experience and live project development.

  • This course enhances your data science skills to help you land your dream job.
  • Through this course, you will get in-depth information about python and its data science library.
  • Career guidance and doubt resolving sessions with industry experts.
  • Learning through hands-on experience and live project development.
  • You will learn in detail about different data sciences techniques like NumPy, Pandas, Seaborn, and Matplotlib.

The Course Provides Shared Expertise by

LSET Trainers

LSET Trainers

Industry Experts

Industry Experts

Top Employers

Top Employers

Skills You will Gain

  • Black-box Testing Techniques
  • White-box Testing Techniques
  • Unit Testing
  • Static Analysis
  • Testing Automation
  • Writing Test Plans
  • Writing Defect Reports
  • Understanding of Testing Theory
  • Cucumber
  • Writing Tests
  • Testing Vocabulary
  • Executing Tests
  • Software Testing
  • Selenium

Complete Learning Experience

This course provides a hands-on, guided learning experience to help you learn the fundamentals practically.
  • We constantly update the curriculum to include the latest releases and features.
  • We focus on teaching the industry's best practices and standards.
  • We let you explore the topics through guided hands-on sessions.
  • We provide industry professional mentor support to every student.
  • We give you an opportunity to work on real world examples.
  • Work with hands-on projects and assignments.
  • We help you build a technical portfolio that you can present to prospective employers.

Reasons to Choose LSET

  • Interactive live sessions by industry experts.
  • Practical classes with project-based learning with hands-on activities.
  • International learning platform to promote collaboration and teamwork.
  • Most up-to-date course curriculum based on current industry demand.
  • Gain access to various e-learning resources.
  • One-to-one attention to ensure maximum participation in the classes.
  • Lifetime career guidance to get the students employed in good companies.
  • Free lifetime membership to the LSET Alumni Club

What Will Be Your Responsibilities?

  • Work creatively in a problem-solving environment.
  • Ask questions and participate in class discussions.
  • Work on assignments and quizzes promptly.
  • Read additional resources on the course topics and ask questions in class.
  • Actively participate in team projects and presentations.
  • Work with the career development department to prepare for interviews
  • Respond promptly to the instructors, student service officers, career development officers, etc.
  • And most importantly, have fun while learning at LSET.
Your Responsibilities

How Does Project-Based Learning Work?

LSET project-based learning model allows students to work on real-world applications and apply their knowledge and skills gained in the course to build high-performing industry-grade applications. As part of this course, students learn agile project management concepts, tools, and techniques to work on the assigned project collaboratively. Each student completes project work individually but is encouraged to enhance their solution by collaborating with their teammates.

Following are the steps involved in the LSET’s project-based learning;

  1. Step 1: Project Idea Discussion

    In this step, students get introduced to the problem and develop a strategy to build the solution.

  2. Step 2: Build Product Backlog

    This step requires students to enhance the existing starter product backlog available in the project. This helps students to think about real-life business requirements and formulate them in good user stories.

  3. Step 3: Design Releases and Sprints

    In this step, students define software releases and plan sprints for each release. Students must go through sprint planning individually and learn about story points and velocity.

  4. Step 4: Unit and Integration Tests

    In this step, students learn to write unit tests to ensure every application part works fine.

  5. Step 5: Use CICD to Deploy

    In this step, students learn to use CICD (Continuous Integration Continuous Delivery) pipeline to build their application as a docker image and deploy it to Kubernetes.

Capstone Project

LSET gives you an opportunity to work on the real world project which will greatly help you to build your technical portfolio

Project Topic: Online Banking

London has been a leading international financial centre since the 19th century. In recent years, London has seen many FinTech start-ups and significant innovations in the banking sector. This project aims to introduce students to the financial industry and technologies used to handle billions of daily transactions. As part of this project, students will learn the current technological advances and build up their knowledge to start a simple banking application. This application uses agile project management practices to build basic functionality. Students will be presented with user stories to create the initial project backlog. Students need to enhance this backlog by adding more relevant user stories and working on them.

LSET emphasises project-based learning as it allows the students to master the course content by going through near real-world work experience. LSET projects are carefully designed to teach the industry-required skills and mindset. It motivates the students on various essential aspects like learning to work in teams, improving communication with peers, taking the initiative to look for innovative solutions, enhancing problem-solving skills, understanding the end user requirements to build user-specific products, etc.

Capstone Projects build students’ confidence in handling projects and applying their newly learned skills to solve real-world problems. This allows the students to reflect upon their learning and find the opportunity to get the most out of the course. Learn more about Capstone Projects here.

Learning Outcome

  • Students will learn to work in an agile environment
  • Students will learn the agile project management terms used in the industry, like product backlog, user stories, story points, epics, etc.
  • Students will learn to use a Git repository and understand the concepts like commit, pull, push, branch, etc.
  • Students will learn to communicate in a team environment and effectively express their ideas.

Guidance and Help

A dedicated project coordinator who can mentor students on the process will be assigned to this project. Students can also avail of the instructor’s hours as and when needed. LSET may get an industry expert with subject-specific experience to help students understand the industry and its challenges.

Execution Process

This project will be carried out in steps. Each step teaches students a specific aspect of the subject and development paradigm. Following are the steps students will follow to complete this project.

Step 1: Project Introduction Self Study [6 days]

In the first step, students will learn about the financial industry and review the project introduction documentation to build up the subject knowledge. This is a self-learning stage; however, instructor hours are available if required.

Step 2: Project Build-up and Environment Setup [2 days]

In this step, students are required to follow the project guide to set up the development environment. The project document guides students to find and connect to the LSET Git repository and install the necessary libraries or tools.

Step 3: Product Backlog and Sprint Planning [2 days]

In this step, students will use the existing product backlog and enhance it per their project scope. Students can seek help from the project coordinator and the instructor. The project coordinator will help students do sprint planning and assign story points to the stories. This process is meant to give students real-world work environment experience. Students can consider this a mock exercise on agile project management practices.

Step 4: User Stories Execution and Development [12 days]

Students will work on the user stories identified in the Step 3 process in this step. Students will write code and algorithms to complete the development objectives. The project coordinator will be available to help students to guide them on the development and answer any questions they may have. Students can also discuss this with the instructor.

Step 5: Testing, Deployment and Completion [5 days]

In this step, students will test and deploy the application to the cloud environment. Students will experience the deployment process in the cloud and learn the best practices. After the successful deployment, students will present their project to the instructor and the external project reviewer. Feedback will be given to the students. Students will have one week to work on the feedback and submit the final copy of the project, which will be sent to the external examiner for evaluation.

Project Presentation

LSET emphasises preparing students for the work environment by allowing them to learn the required soft skills. After completing the project, students must present their work to the instructor and an invited project reviewer panel. Please note that the assigned external examiner will not be part of this panel and hence will not know about the students. This ensures an unbiased assessment by the external examiner. This exercise aims to allow students to experience an environment they may face in their actual job. Also, it gives them a chance to get feedback from industry experts who can guide students on various parts of the project. This will help students to learn and fix anything they find necessary in their project. This ensures quality output and allows students to learn about industry requirements.

The instructor and the project reviewer panel will assess the students on the following;

Project Repository on GitHub [10 points]: The instructor will ensure that the students have uploaded the project repository to the LSET’s GitHub account per the guidelines in the project requirement documentation. Full points will be awarded if the repository is appropriately set up per the instructions.

Presentation Skills [20 points]: Students must present their work in the given timeframe. Full points will be awarded if students cover everything needed to deliver their work in the given timeframe.

Communication Skills [20 points]: Students must present their work in a manner understandable by all the participants. More focus will be given to how students communicate, not the language. Full points will be awarded if students can share their work correctly.

Evaluation Criteria

LSET promotes a transparent and unbiased evaluation process. All the external examiners will follow a set process to grade students. No student’s personal or identifying information will be shared with the external examiners, so they will not know about the person they are grading. They will only get the project files and grading guidelines to follow. This will ensure equal quality standards across the institute.

Following are some critical areas the LSET external examiners will be grading on.

Project Documentation [10 points]: Project documentation is filed correctly with the information which can be used to understand the project work. Students can use the supplied project documentation template to fill up the data. External examiner to confirm if all the information is filled up. Full points will be awarded if all the sections are covered.

Project Structure [10 points]: Students must follow the proper structure while developing their projects. This structure is being taught and covered in the project requirement documentation. External examiner to confirm if the project files are correctly structured. Full points will be awarded if the structure meets the given guideline.

Solves Basic Problem [50 points]: Students must ensure that they implement all the requirements in the project documentation. External examiner to confirm if the project solves the given problem. Full points will be awarded if the students include everything asked in the project requirement.

Innovation [20 points]: Students are encouraged to bring new ideas into their development. They can improve the design, use new design patterns, code with a better coding style, or add a feature. External examiner to confirm if the students have added more than the requirement to improve the design or solution. The new addition must include a new feature and should not be similar to the requirements given. Full points will be awarded if the external examiner finds an innovation or see students going beyond the asked requirements.

Best Practices [20 points]: Students must follow the best practices in their development. This will help them to become a quality resource for their prospective employer. External examiner to confirm if the supplied best practices are followed in the project. Full points will be awarded if the best practices are properly implemented.

Performance Consideration [20 points]: Students must consider performance while working on their projects. Performance is one of the critical industry requirements. External examiner to confirm if the student thought the performance improvements in the project. Full points will be awarded if the external examiner sees efforts taken to consider performance aspects in the development.

Security Structure [20 points]: Students need to consider the security aspect If applicable in the design and development. External examiner to confirm if the security consideration is appropriate in this project; if it is applicable, the examiner to verify if the student has considered the security elements in the project. Full points will be awarded if the external examiner sees efforts taken to assess the security aspect of the development.

Benefits of LSET Certificate

Earning the LSET Certificate means you have demonstrated hard-working capabilities and learnt the latest technologies by completing hands-on exercises and real-world projects.

Following are some of the traits employers can trust you have built up through your course;
  • You know how to work in a team environment and communicate well.
  • You know the tools which are necessary for your desired job.
  • You know how to use the latest technologies to develop technologically advanced solutions.
  • You have developed problem-solving skills to navigate complex problem scenarios and find the right solutions.
  • You are now ready to take on the challenge and help your prospective employer to build the desired solutions.
Benefits of LSET Certificate
What to expect after completing the course

What to expect after completing the course?

After earning your certificate from LSET, you can join the LSET’s Alumni club. There are countless benefits associated with the Alumni Club membership. As a member of LSET Alumni, you can expect the following;
  • LSET to hold your hand to find a successful career
  • Advice you on choosing the right job based on your passion and goals
  • Connect you with industry experts for career progression
  • Provide you opportunities to participate in events to keep yourself updated
  • Provide you with a chance to contribute to the game-changing open-source projects
  • Provide you with a platform to shine by allowing you to speak at our events

Tools & Technologies You Will Learn from This Course

TensorFlow

NumPy

SciPy

Pandas

Keras

PyTorch

Register Now!

Start Your Journey to becoming a Professional Data Scientist

LSET could provide the perfect headstart to start your career in Data Science with Python.

Apply Now

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