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Data Science with Python

you will learn about data analysis, machine learning, data visualization, web scraping, & natural language processing.
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Course Duration: 45 Hours
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Data Science is a multifaceted field used to gain insights from complex data. Data Science is a blend of various tools, algorithms, and machine learning principles with the goal to discover hidden patterns from the raw data.

Data Science is primarily used to make decisions and predictions making use of predictive causal analytics, prescriptive analytics (predictive plus decision science) and machine learning. Data Science can help you make informed decisions, create beautiful visualizations, and even try to predict future events through Machine Learning.

 

Data science is just about as broad of a term as they come. It may be easiest to describe what it is by listing its more concrete components i.e. Data exploration & analysis : Pandas; NumPy; SciPy; a helping hand from Python’s Standard Library and Data visualization - Taking data and turning it into something colorful : Matplotlib; Seaborn; Datashader.

Data science iencompasses a wide range of fields: Mathematics, Statistics, Python, Applying advanced statistical techniques in Python, Data Visualization, Machine Learning, Deep Learning, and the like.

 

Companies are looking for data-driven decision makers, and this Data Science course by Uplatz, taught by our elite tutor Imad Jaweed, will teach you the skills you need to become just that. You'll learn to analyze data, communicate your findings, and even draw predictions using machine learning. Along the way, you'll build portfolio-worthy projects that will help you get job-ready.

Uplatz’s Data Science with Python course will help you to master the concepts of implementing Data Science with Python programming. Through this Python for Data Science training, you will gain knowledge in data analysis, machine learning, data visualization, web scraping, & natural language processing.

Upon Data Science course completion, you will get a certificate issued by Uplatz.

Course/Topic - Data Science with Python - all lectures

  • In this video tutorial we will get introduced to Data Science and the integration of Python in Data Science. Furthermore, we will look into the importance of Data Science and its demand and the application of Data Science.

    • 1:01:14

  • In this video we will learn, all the concepts of Python programming related to Data Science. We will also learn about the Introduction to Python Programing, what is Python Programming and its History, Features and Application of Python along with its setup. Further we will see how to get started with the first python program.

    • 59:19

  • This video talks about the Variable and Data Types in Python Programming. In this session we will learn What is variable, the declaration of variable and variable assignment. Further we will see the data types in python, checking data types and data type conversions.

    • 27:05

  • This tutorial will help you to understand Data Types in python in depth. This video talks about the data types such as numbers, sequence type, Boolean, set and dictionary.

    • 55:27

  • This tutorial talks about the Identifier, keyword, reading input and output formatting in Data Science. We will learn about what is an identifier and keywords. Further we will learn about reading input and taking multiple inputs from a user, Output formatting and Python end parameter.

    • 49:19

  • This tutorial talks about taking multiple inputs from user and output formatting using format method, string method and module operator.

    • 44:09

  • This tutorial talks about the Operators and type of operators. In this session we will learn about the types of operators such as arithmetic, Relational and Assignment Operators.

    • 27:52

  • This tutorial talks further about the part 2 of operators and its types. In this session we will learn about the types of operators such as Logical, Membership, Identity and Bitwise Operators.

    • 31:22

  • In this video you will learn about the process of decision making in Data Science. Furthermore, this tutorial talks about different types of decision-making statements and its application in Data Science.

    • 45:23

  • In this video tutorial we will learn about the Loops in Python programing. We will cover further the different types of Loops in Python, starting with: For Loop.

    • 32:47

  • In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: While loop and nested loop.

    • 39:43

  • In this session we will cover the further part of loops in Python programming. The type of loops explained in this video is: break, continue and pass loops

    • 23:13

  • In this video tutorial we will start explaining about the lists in Python Programming. This tutorial talks about accessing values in the list and updating the list in Data Science.

    • 46:54

  • In this video tutorial we will look into the further parts about the lists in Python Programming. Deleting list elements, basic list operations, built in functions and methods and the features which are covered in this session.

    • 40:30

  • This tutorial will cover the basics on Tuples and Dictionary function in Data Science. We will learn about accessing and deleting tuple elements. Further we will also cover the basic tuples operations and the built in tuple functions and its methods. At the end we will see the differences in list and tuple.

    • 53:32

  • This tutorial will cover the advanced topics on Tuples and Dictionary function in Data Science. Further in this session we will learn about the Python Dictionary, how to access, update and delete dictionary elements. Lastly we will cover built in functions and methods.

    • 51:22

  • In this session we will learn about the functions and modules used in Data science. After watching this video, you will be able to understand what is a function, the definition of function and calling a function.

    • 44:01

  • In this session we will learn about the further functions and modules used in Data science. After watching this video, you will be able to understand the ways to write a function, Types of functions, Anonymous Functions and Recursive functions.

    • 43:16

  • In this session we will learn about the advanced functions and modules used in Data science. After watching this video, you will be able to understand what is a module, creating a module, import statement and locating modules.

    • 48:21

  • This tutorial talks about the features of working with files. In this video we will learn about opening and closing file, the open function, the file object Attributes, the close method, reading and writing files.

    • 1:05:09

  • This tutorial talks about the advanced features of working with files. In this video we will learn about file positions, renaming and deleting files.

    • 26:50

  • In this session we will learn about the regular expression. After this video you will be able to understand what is a regular expression, meta characters, match function, search function, Re- match vs research, split function and sub function.

    • 1:02:45

  • This video introduces you to the Data Science Libraries. In this video you will learn about the Data science libraries: libraries for data processing, modelling and data visualization.

    • 45:35

  • In this session we will teach about the components of python ecosystem in Data Science. This video talks about the Components of Python Ecosystem using package Python distribution Anaconda and jupyter notebook.

    • 54:24

  • This tutorial talks about the basics of analyzing data using numpy and pandas. The Pandas module mainly works with the tabular data, whereas the NumPy module works with the numerical data. We will further see what is Numpy and why we use numpy.

    • 56:58

  • This tutorial talks about the later part of analyzing data using numpy and pandas. In this tutorial we will learn how to install numpy.

    • 43:37

  • This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn what is Pandas and the key features of Pandas. We will also learn about the Python Pandas environment setup.

    • 37:21

  • This tutorial talks about the advanced part of analyzing data using numpy and pandas. In this session we will learn about Pandas data structure with example.

    • 1:12:53

  • This the last session on Analysing Data using Numpy and Pandas. In this session we will learn data analysis using Pandas

    • 28:31

  • In this video tutorial we will learn about the Data Visualization using Matpotlib. This video talks about what is data visualisation, introduction to matplotlib and installation of matplotlib.

    • 37:45

  • In this session we will see the part 2 of Data Visualization with Matplotlib. This video talks about the types of data visualization charts and line chart scatter plot

    • 43:41

  • This tutorial covers part 3 of Data Visualization with Matplotlib. This session covers the types of data visualisation charts: bar chart histogram, area plot pie chart and box plot contour plot.

    • 1:09:26

  • This session talks about the Three-Dimensional Plotting with Matplotlib . In this we will learn about plot 3D scatter, plot 3D contour and plot 3D surface plot.

    • 1:03:43

  • In this tutorial we will cover basics of Data Visualisation with Seaborn. Further we will cover Introduction to seaborn, seaborn functionalities, how to install seaborn and the different categories of plot in seaborn

    • 41:53

  • In this tutorial we will cover the advanced topics of Data Visualisation with Seaborn. In this video we will see about exploring seaborn plots.

    • 59:16

  • Introduction to Statistical Analysis is taught in this video. We will learn what is statistical analysis and introduction to math and statistics for data science. Further we will learn about the terminologies in statistics for data science and categories in statistics, its correlation and lastly mean median and mode quartile.

    • 1:15:05

  • This video course talks about the basics of Data Science methodology. We will learn how to reach from problem to approach.

    • 47:33

  • In this session we will see Data Science Methodology from requirements to collection and from understanding to preparation.

    • 44:12

  • In this session we will learn advanced Data Science Methodology from modelling to evaluation and from deployment to feedback.

    • 39:25

  • This video tutorial talks about the - Introduction to Machine Learning and its Types. In this session we will learn what is machine learning and the need for machine learning. Further we will see the application of machine learning and different types of machine learning. We will also cover topics such as supervised learning, unsupervised learning and reinforcement learning.

    • 56:17

  • This video tutorial talks about the basics of regression analysis. We will cover in this video linear regression and implementing linear regression.

    • 1:11:51

  • This video tutorial talks about the further topics of regression analysis. In this video we will learn about multiple linear regression and implementing multiple linear regression.

    • 54:25

  • This video tutorial talks about the advanced topics of regression analysis. In this video we will learn about polynomial regression and implementing polynomial regression.

    • 38:18

  • In this session we will learn about the classification in Data science. We will see what is classification, classification algorithms and Logistic regression. Also we will learn about implementing Logistic regression.

    • 1:08:43

  • In this session we will learn about the further topics of classification in Data science, such as decision tree and implementing decision tree.

    • 38:36

  • In this session we will learn about the advanced topics of classification in Data science, such as support vendor machine and implementing support vector machine.

    • 25:37

  • This tutorial will teach you about what is clustering and clustering algorithms. Further we will learn what K means clustering and how does K means clustering work and also about implementing K means clustering.

    • 53:10

  • In this session we will see the further topics of clustering, such as hierarchical clustering, agglomerative hierarchical clustering, how does agglomerative hierarchical clustering Work and divisive hierarchical clustering.

    • 28:55

  • This video tutorial talks about the advanced topics of clustering, such as implementation of agglomerative hierarchical clustering.

    • 33:58

  • This video will help you to understand basics of Association rule learning. In this session we will learn about the Apriori algorithm and the working of Apriori algorithm.

    • 53:30

  • This video will help you to understand advanced topics of Association rule learning such as implementation of Apriori algorithm.

    • 58:45

  • This is a session on the practical part of Data Science application. In this example we will see problem statement, data set, exploratory data analysis.

    • 56:40

  • This is a session on the practical part of Data Science application.

    • 42:39

  • This is a session on the practical part of Data Science application. In this we will see the implementation of the project.

    • 50:54

  • This is a session on the practical part of Data Science application

    • 38:18

  • This is a session on the practical part of Data Science application

    • 1:02:31
Course Objectives Back to Top

·       Understand Python language basics and how they apply to data science

·       Analyze data using Python libraries like pandas and numpy

·       Create stunning data visualizations with matplotlib, folium, and seaborn

·       Build machine learning models using scipy and scikitlearn

·       Demonstrate proficiency in solving real life data science problems

Course Syllabus Back to Top

1. Introduction to Data Science

  • Introduction to Data Science

  • Python in Data Science

  • Why is Data Science so Important?

  • Application of Data Science

  • What will you learn in this course?

 

2. Introduction to Python Programming

  • What is Python Programming?

  • History of Python Programming

  • Features of Python Programming

  • Application of Python Programming

  • Setup of Python Programming

  • Getting started with the first Python program

 

3. Variables and Data Types

  • What is a variable?

  • Declaration of variable

  • Variable assignment

  • Data types in Python

  • Checking Data type

  • Data types Conversion

  • Python programs for Variables and Data types

     

4. Python Identifiers, Keywords, Reading Input, Output Formatting

  • What is an Identifier?

  • Keywords

  • Reading Input

  • Taking multiple inputs from user

  • Output Formatting

  • Python end parameter

 

5. Operators in Python

  • Operators and types of operators

          - Arithmetic Operators

          - Relational Operators

          - Assignment Operators

          - Logical Operators

          - Membership Operators

          - Identity Operators

          - Bitwise Operators

  • Python programs for all types of operators

 

6. Decision Making

  • Introduction to Decision making

  • Types of decision making statements

  • Introduction, syntax, flowchart and programs for

       - if statement

       - if…else statement

       - nested if

  • elif statement

 

7. Loops

  • Introduction to Loops

  • Types of loops

       - for loop

       - while loop

       - nested loop

  • Loop Control Statements

  • Break, continue and pass statement

  • Python programs for all types of loops

 

8. Lists

  • Python Lists

  • Accessing Values in Lists

  • Updating Lists

  • Deleting List Elements

  • Basic List Operations

  • Built-in List Functions and Methods for list

 

9. Tuples and Dictionary

  • Python Tuple

  • Accessing, Deleting Tuple Elements

  • Basic Tuples Operations

  • Built-in Tuple Functions & methods

  • Difference between List and Tuple

  • Python Dictionary

  • Accessing, Updating, Deleting Dictionary Elements

  • Built-in Functions and Methods for Dictionary

 

10. Functions and Modules

  • What is a Function?

  • Defining a Function and Calling a Function

  • Ways to write a function

  • Types of functions

  • Anonymous Functions

  • Recursive function

  • What is a module?

  • Creating a module

  • import Statement

  • Locating modules

 

11. Working with Files

  • Opening and Closing Files

  • The open Function

  • The file Object Attributes

  • The close() Method

  • Reading and Writing Files

  • More Operations on Files

 

12. Regular Expression

  • What is a Regular Expression?

  • Metacharacters

  • match() function

  • search() function

  • re.match() vs re.search()

  • findall() function

  • split() function

  • sub() function

 

13. Introduction to Python Data Science Libraries

  • Data Science Libraries

  • Libraries for Data Processing and Modeling

      - Pandas

      - Numpy

      - SciPy

      - Scikit-learn

  • Libraries for Data Visualization

      - Matplotlib

      - Seaborn

      - Plotly

 

14. Components of Python Ecosystem

  • Components of Python Ecosystem

  • Using Pre-packaged Python Distribution: Anaconda

  • Jupyter Notebook

 

15. Analysing Data using Numpy and Pandas

  • Analysing Data using Numpy & Pandas

  • What is numpy? Why use numpy?

  • Installation of numpy

  • Examples of numpy

  • What is ‘pandas’?

  • Key features of pandas

  • Python Pandas - Environment Setup

  • Pandas – Data Structure with example

  • Data Analysis using Pandas

 

16. Data Visualisation with Matplotlib

  • Data Visualisation with Matplotlib

      - What is Data Visualisation?

      - Introduction to Matplotlib

      - Installation of Matplotlib

  • Types of data visualization charts/plots

      - Line chart, Scatter plot

      - Bar chart, Histogram

      - Area Plot, Pie chart

      - Boxplot, Contour plot

 

17. Three-Dimensional Plotting with Matplotlib

  • Three-Dimensional Plotting with Matplotlib

      - 3D Line Plot

      - 3D Scatter Plot

      - 3D Contour Plot

      - 3D Surface Plot

 

18. Data Visualisation with Seaborn

  • Introduction to seaborn

  • Seaborn Functionalities

  • Installing seaborn

  • Different categories of plot in Seaborn

  • Exploring Seaborn Plots

 

19. Introduction to Statistical Analysis

  • What is Statistical Analysis?

  • Introduction to Math and Statistics for Data Science

  • Terminologies in Statistics – Statistics for Data Science

  • Categories in Statistics

  • Correlation

  • Mean, Median, and Mode

  • Quartile

 

20. Data Science Methodology (Part-1)

Module 1: From Problem to Approach

  • Business Understanding

  • Analytic Approach

Module 2: From Requirements to Collection

  • Data Requirements

  • Data Collection

Module 3: From Understanding to Preparation

  • Data Understanding

  • Data Preparation

 

21. Data Science Methodology (Part-2)

Module 4: From Modeling to Evaluation

  • Modeling

  • Evaluation

Module 5: From Deployment to Feedback

  • Deployment

  • Feedback

Summary

 

22. Introduction to Machine Learning and its Types

  • What is a Machine Learning?

  • Need for Machine Learning

  • Application of Machine Learning

  • Types of Machine Learning

      - Supervised learning

      - Unsupervised learning

      - Reinforcement learning

 

23. Regression Analysis

  • Regression Analysis

  • Linear Regression

  • Implementing Linear Regression

  • Multiple Linear Regression

  • Implementing Multiple Linear Regression

  • Polynomial Regression

  • Implementing Polynomial Regression

 

24. Classification

  • What is Classification?

  • Classification algorithms

  • Logistic Regression

  • Implementing Logistic Regression

  • Decision Tree

  • Implementing Decision Tree

  • Support Vector Machine (SVM)

  • Implementing SVM

 

25. Clustering

  • What is Clustering?

  • Clustering Algorithms

  • K-Means Clustering

  • How does K-Means Clustering work?

  • Implementing K-Means Clustering

  • Hierarchical Clustering

  • Agglomerative Hierarchical clustering

  • How does Agglomerative Hierarchical clustering Work?

  • Divisive Hierarchical Clustering

  • Implementation of Agglomerative Hierarchical Clustering

 

26. Association Rule Learning

  • Association Rule Learning

  • Apriori algorithm

  • Working of Apriori algorithm

  • Implementation of Apriori algorithm

Certification Back to Top

Python certifications are a kind of credential. Credentials are largely how the traditional education system works. Employers use that credential as a proxy for experience when deciding whether to hire you. It’s proof that you’ve learned the things you say you’ve learned.

As less-conventional paths to education like MOOCs have become more popular, they have replicated the credential that comes with a degree by offering online certificates. Additionally, many stand-alone companies offer certifications that aim to replicate the degree credential.

The Python Institute is committed to the development of an independent global standard in Python programming certification, which will allow programming specialists, software developers, and IT professionals from all over the world to assess and document their programming skills objectively, and to gain recognition for their expertise.

The Python Institute offers perhaps some of the most well-known Python certifications. It offers four primary certificate level exams:

• Certified Entry-Level Python Programmer (PCEP) — 

• Certified Associate in Python Programming (PCAP) — 

• Certified Professional in Python Programming 1 (PCPP-32-1) — 

• Certified Professional in Python Programming 2 (PCPP-32-2) — 

These certifications are progressive, meaning that you're meant to earn PCEP before PCAP (and so on), and in many cases the previous-level certificate is required to sit for the next-level certification exam.

Microsoft offers an entry-level Python certification exam called Introduction to Programming Using Python. 

 

Career & Jobs Back to Top

Data Science is now being integrated with industries across all sectors. That’s why not only are the data scientists expected to have a broader set of skills, but the employers also expect more cohesive specialization and collaboration.

Data scientists can expect to make an average of $117,345 per year. But that number can vary based on where a data scientist works, or their years of experience. For example, a data scientist working at a company with up to 500 employees can expect to earn $112,365 per year, while a data scientist with 15-plus years of experience can earn an average of $141,921 a year.

Data Science is considered one of the most lucrative jobs in the industry right now. With numerous openings spanning across all sectors, data science jobs are showing only the signs of growth. As more and more companies are adopting data science, companies are hiring data scientists by hordes. However, despite India being a frontrunner in technical education and research, the demand-supply gap for data science jobs vs applicants is only widening.

Job Roles in Data Science

• Data Analyst

• Data Engineers

• Database Administrator

• Machine Learning Engineer

• Data Scientist

• Data Architect

• Statistician

• Business Analyst

• Data and Analytics Manager

 

Interview Questions Back to Top

Q1. What built-in data types are used in Python?

Python uses several built-in data types, including:

Number (int, float and complex)

String (str)

Tuple (tuple)

Range (range)

List (list)

Set (set)

Dictionary (dict)

In Python, data types are used to classify or categorize data, and every value has a data type.

 

Q2. How are data analysis libraries used in Python? What are some of the most common libraries?

 

A key reason Python is such a popular data science programming language is because there is an extensive collection of data analysis libraries available. These libraries include functions, tools and methods for managing and analyzing data. There are Python libraries for performing a wide range of data science functions, including processing image and textual data, data mining and data visualization. The most widely used Python data analysis libraries include:

 

Pandas

NumPy

SciPy

TensorFlow

SciKit

Seaborn

Matplotlib

Q3. How is a negative index used in Python?

Negative indexes are used in Python to assess and index lists and arrays from the end, counting backwards. For example, n-1 will show the last item in a list, while n-2 will show the second to last. Here’s an example of a negative index in Python:

 

b = "Python Coding Fun"

print(b[-1])

>> n

Q4. What’s the difference between lists and tuples in Python?

Lists and tuples are classes in Python that store one or more objects or values. Key differences include:

Syntax – Lists are enclosed in square brackets and tuples are enclosed in parentheses.

Mutable vs. Immutable – Lists are mutable, which means they can be modified after being created. Tuples are immutable, which means they cannot be modified.

Operations – Lists have more functionalities available than tuples, including insert and pop operations and sorting.

Size – Because tuples are immutable, they require less memory and are subsequently faster.

Q5. Write a function to generate N samples from a normal distribution and plot them on the histogram.

This is a relatively simple problem. We have to set up our distribution and generate N samples from it, which are then plotted. In this question, we make use of the SciPy library which is a library made for scientific computing.

Q6. Given a list of stock prices in ascending order by datetime, write a function that outputs the max profit by buying and selling at a specific interval.

 

Example:

stock_prices = [10,5,20,32,25,12]

get_max_profit(stock_prices) -> 27

Making it harder, given a list of stock prices and date times in ascending order by datetime, write a function that outputs the profit and start and end dates to buy and sell for max profit.

 

stock_prices = [10,5,20,32,25,12]

dts = [

'2019-01-01',

'2019-01-02',

'2019-01-03',

'2019-01-04',

'2019-01-05',

'2019-01-06',

]

get_profit_dates(stock_prices,dts) -> (27, '2019-01-02', '2019-01-04')

 

Q7. Amy and Brad take turns rolling a fair six-sided die. Whoever rolls a “6” first wins the game. Amy starts by rolling first. What’s the probability that Amy wins?

Given this scenario, we can write a Python function that can simulate this scenario thousands of times to see how many times Amy wins first. Solving this problem then requires understanding how to create two separate people and simulate the scenario of one person rolling first each time.

Amy and Brad take turns in rolling a fair six-sided die. Whoever rolls a "6" first wins the game. Amy starts by rolling first.What's the probability that Amy wins?

Q8. Every night between 7pm and midnight, two computing jobs from two different sources are randomly started with each one lasting an hour. Unfortunately, when the jobs simultaneously run, they cause a failure in some of the company’s other nightly jobs, resulting in downtime for the company that costs $1,000.

Q9. Imagine a deck of 500 cards numbered from 1 to 500. If all the cards are shuffled randomly and you are asked to pick three cards, one at a time, what's the probability of each subsequent card being larger than the previous drawn card?

One way to start this question: Make the sample smaller. Let's say the question is actually 100 cards and you select 3 cards without replacement. Does the answer change?

Course Quiz Back to Top
Start Quiz
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A14. Premium Career tracks are programs consisting of video courses that lead to skills required by C-suite executives such as CEO, CTO, CFO, and so on. These programs will help you gain knowledge and acumen to become a senior management executive.

Q15. How unlimited subscription works?
A15. Uplatz offers 2 types of unlimited subscription, Monthly and Yearly. Our monthly subscription give you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews each month. Minimum committment is for 1 year, you can cancel anytime after 1 year of enrolment. Our yearly subscription gives you unlimited access to our more than 300 video courses with 6000 hours of learning content. The plan renews every year. Minimum committment is for 1 year, you can cancel the plan anytime after 1 year. Check our monthly and yearly subscription here https://training.uplatz.com/online-it-courses.php?search=subscription

Q16. Do you provide software access with video course?
A16. Software access can be purchased seperately at an additional cost. The cost varies from course to course but is generally in between GBP 20 to GBP 40 per month.

Q17. Does your course guarantee a job?
A17. Our course is designed to provide you with a solid foundation in the subject and equip you with valuable skills. While the course is a significant step toward your career goals, its important to note that the job market can vary, and some positions might require additional certifications or experience. Remember that the job landscape is constantly evolving. We encourage you to continue learning and stay updated on industry trends even after completing the course. Many successful professionals combine formal education with ongoing self-improvement to excel in their careers. We are here to support you in your journey!

Q18. Do you provide placement services?
A18. While our course is designed to provide you with a comprehensive understanding of the subject, we currently do not offer placement services as part of the course package. Our main focus is on delivering high-quality education and equipping you with essential skills in this field. However, we understand that finding job opportunities is a crucial aspect of your career journey. We recommend exploring various avenues to enhance your job search:
a) Career Counseling: Seek guidance from career counselors who can provide personalized advice and help you tailor your job search strategy.
b) Networking: Attend industry events, workshops, and conferences to build connections with professionals in your field. Networking can often lead to job referrals and valuable insights.
c) Online Professional Network: Leverage platforms like LinkedIn, a reputable online professional network, to explore job opportunities that resonate with your skills and interests.
d) Online Job Platforms: Investigate prominent online job platforms in your region and submit applications for suitable positions considering both your prior experience and the newly acquired knowledge. e.g in UK the major job platforms are Reed, Indeed, CV library, Total Jobs, Linkedin.
While we may not offer placement services, we are here to support you in other ways. If you have any questions about the industry, job search strategies, or interview preparation, please dont hesitate to reach out. Remember that taking an active role in your job search process can lead to valuable experiences and opportunities.

Q19. How do I enrol in Uplatz video courses?
A19. To enroll, click on "Buy This Course," You will see this option at the top of the page.
a) Choose your payment method.
b) Stripe for any Credit or debit card from anywhere in the world.
c) PayPal for payments via PayPal account.
d) Choose PayUmoney if you are based in India.
e) Start learning: After payment, your course will be added to your profile in the student dashboard under "Video Courses".

Q20. How do I access my course after payment?
A20. Once you have made the payment on our website, you can access your course by clicking on the "My Courses" option in the main menu or by navigating to your profile, then the student dashboard, and finally selecting "Video Courses".

Q21. Can I get help from a tutor if I have doubts while learning from a video course?
A21. Tutor support is not available for our video course. If you believe you require assistance from a tutor, we recommend considering our live class option. Please contact our team for the most up-to-date availability. The pricing for live classes typically begins at USD 999 and may vary.
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