Data Science with Python Course

Get hands-on Data Science with Python skills and accelerate your data science career

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit
  • Create robust predictive models with advanced statistics
  • Leverage hypothesis testing and inferential statistics for sound decision-making
  • 400,000 + Professionals Trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Grow your Data Science Skills with Python

This four-week course is ideal for learning Data Science with Python even for beginners. Get hands-on programming experience in Python that you'll be able to immediately apply in the real world. Equip yourself with the skills you need to work with large data sets, build predictive models and tell a compelling story to stakeholders.

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  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries


Data Science has bagged the top spot in LinkedIn’s Emerging Jobs Report for the last three years. Thousands of companies need team members who can transform data sets into strategic forecasts. Acquire in-demand data science and Python skills and meet that need. Data Science with Python skills will help you to be future-ready.

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The KnowledgeHut Edge

Learn by Doing

Our immersive learning approach lets you learn by doing and acquire immediately applicable skills hands-on.

Real-World Focus

Learn theory backed by real-world practical case studies and exercises. Skill up and get productive from the get-go.

Industry Experts

Get trained by leading practitioners who share best practices from their experience across industries.

Curriculum Designed by the Best

Our Data Science advisory board regularly curates best practices to emphasize real-world relevance.

Continual Learning Support

Webinars, e-books, tutorials, articles, and interview questions - we're right by you in your learning journey!

Exclusive Post-Training Sessions

Six months of post-training mentor guidance to overcome challenges in your Data Science career.


Prerequisites for the Data Science with Python training program

  • There are no prerequisites to attend the Data Science with Python course.
  • Elementary programming knowledge will be of advantage.

Who should attend the Data Science with Python course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

Python Distribution

Anaconda, basic data types, strings, regular expressions, data structures, loops, and control statements.

User-defined functions in Python

Lambda function and the object-oriented way of writing classes and objects.

Datasets and manipulation

Importing datasets into Python, writing outputs and data analysis using Pandas library.

Probability and Statistics

Data values, data distribution, conditional probability, and hypothesis testing.

Advanced Statistics

Analysis of variance, linear regression, model building, dimensionality reduction techniques.

Predictive Modelling

Evaluation of model parameters, model performance, and classification problems.

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Data Science with Python Course Curriculum

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Learning objectives
Understand the basics of Data Science and gauge the current landscape and opportunities. Get acquainted with various analysis and visualization tools used in data science.


  • What is Data Science?
  • Data Analytics Landscape
  • Life Cycle of a Data Science Project
  • Data Science Tools and Technologies 

Learning objectives
The Python module will equip you with a wide range of Python skills. You will learn to:

  • To Install Python Distribution - Anaconda, basic data types, strings, and regular expressions, data structures and loops, and control statements that are used in Python
  • To write user-defined functions in Python
  • About Lambda function and the object-oriented way of writing classes and objects 
  • How to import datasets into Python
  • How to write output into files from Python, manipulate and analyse data using Pandas library
  • Use Python libraries like Matplotlib, Seaborn, and ggplot for data visualization


  • Python Basics
  • Data Structures in Python 
  • Control and Loop Statements in Python
  • Functions and Classes in Python
  • Working with Data
  • Data Analysis using Pandas
  • Data Visualisation
  • Case Study


  • How to install Python distribution such as Anaconda and other libraries
  • To write python code for defining as well as executing your own functions
  • The object-oriented way of writing classes and objects
  • How to write python code to import dataset into python notebook
  • How to write Python code to implement Data Manipulation, Preparation, and Exploratory Data Analysis in a dataset

Learning objectives
In the Probability and Statistics module you will learn:

  • Basics of data-driven values - mean, median, and mode
  • Distribution of data in terms of variance, standard deviation, interquartile range
  • Basic summaries of data and measures and simple graphical analysis
  • Basics of probability with real-time examples
  • Marginal probability, and its crucial role in data science
  • Bayes’ theorem and how to use it to calculate conditional probability via Hypothesis Testing
  • Alternate and Null hypothesis - Type1 error, Type2 error, Statistical Power, and p-value


  • Measures of Central Tendency
  • Measures of Dispersion 
  • Descriptive Statistics 
  • Probability Basics
  • Marginal Probability
  • Bayes Theorem
  • Probability Distributions
  • Hypothesis Testing


  • How to write Python code to formulate Hypothesis
  • How to perform Hypothesis Testing on an existent production plant scenario

Learning objectives
Explore the various approaches to predictive modelling and dive deep into advanced statistics:

  • Analysis of Variance (ANOVA) and its practicality
  • Linear Regression with Ordinary Least Square Estimate to predict a continuous variable
  • Model building, evaluating model parameters, and measuring performance metrics on Test and Validation set
  • How to enhance model performance by means of various steps via processes such as feature engineering, and regularisation
  • Linear Regression through a real-life case study
  • Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis
  • Various techniques to find the optimum number of components or factors using screen plot and one-eigenvalue criterion, in addition to a real-Life case study with PCA and FA.


  • Analysis of Variance (ANOVA)
  • Linear Regression (OLS)
  • Case Study: Linear Regression
  • Principal Component Analysis
  • Factor Analysis
  • Case Study: PCA/FA


  • With attributes describing various aspect of residential homes for which you are required to build a regression model to predict the property prices
  • Reducing Dimensionality of a House Attribute Dataset to achieve more insights and better modelling

Learning objectives
Learning Data Science with Python will help you to understand and execute advanced concepts. Take your advanced statistics and predictive modelling skills to the next level in this module covering:

  • Binomial Logistic Regression for Binomial Classification Problems
  • Evaluation of model parameters
  • Model performance using various metrics like sensitivity, specificity, precision, recall, ROC Curve, AUC, KS-Statistics, and Kappa Value
  • Binomial Logistic Regression with a real-life case Study
  • KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K
  • KNN through a real-life case study
  • Decision Trees - for both regression and classification problem
  • Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID
  • Using Decision Tree with real-life Case Study


  • Logistic Regression
  • Case Study: Logistic Regression
  • K-Nearest Neighbour Algorithm
  • Case Study: K-Nearest Neighbour Algorithm
  • Decision Tree
  • Case Study: Decision Tree


  • Building a classification model to predict which customer is likely to default a credit card payment next month, based on various customer attributes describing customer characteristics
  • Predicting if a patient is likely to get any chronic kidney disease depending on the health metrics
  • Building a model to predict the Wine Quality using Decision Tree based on the ingredients’ composition

Learning objectives
All you need to know to work with time series data with practical case studies and hands-on exercises. You will:

  • Understand Time Series Data and its components - Level Data, Trend Data, and Seasonal Data
  • Work on a real-life Case Study with ARIMA.


  • Understand Time Series Data
  • Visualizing Time Series Components
  • Exponential Smoothing
  • Holt's Model
  • Holt-Winter's Model
  • Case Study: Time Series Modelling on Stock Price


  • Writing python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Writing python code to Use Holt's model when your data has Constant Data, Trend Data and Seasonal Data. How to select the right smoothing constants.
  • Writing Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Use ARIMA to predict the stock prices based on the dataset including features such as symbol, date, close, adjusted closing, and volume of a stock.

Learning objectives
This industry-relevant capstone project under the experienced guidance of an industry expert is the cornerstone of this applied Data Science with Python course. In this immersive learning mentor-guided live group project, you will go about executing the data science project as you would any business problem in the real-world.


  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

The Data Science with Python course has been thoughtfully designed to make you a dependable Data Scientist ready to take on significant roles in top tech companies. At the end of the course, you will be able to:

  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Data visualization with Python libraries: Matplotlib, Seaborn, and ggplot
  • Distribution of data: variance, standard deviation, interquartile range
  • Calculating conditional probability via Hypothesis Testing
  • Analysis of Variance (ANOVA)
  • Building linear regression models, evaluating model parameters, and measuring performance metrics
  • Using Dimensionality Reduction Technique
  • Building Binomial Logistic Regression models, evaluating model parameters, and measuring performance metrics
  • Building KNN algorithm models to find the optimum value of K
  • Building Decision Tree models for both regression and classification problems
  • Build Python programs: distribution, user-defined functions, importing datasets and more
  • Manipulate and analyse data using Pandas library
  • Visualize data with Python libraries: Matplotlib, Seaborn, and ggplot
  • Build data distribution models: variance, standard deviation, interquartile range
  • Calculate conditional probability via Hypothesis Testing
  • Perform analysis of variance (ANOVA)
  • Build linear regression models, evaluate model parameters, and measure performance metrics
  • Use Dimensionality Reduction
  • Build Logistic Regression models, evaluate model parameters, and measure performance metrics
  • Perform K-means Clustering and Hierarchical Clustering
  • Build KNN algorithm models to find the optimum value of K
  • Build Decision Tree models for both regression and classification problems
  • Build data visualization models for Time Series data and components
  • Perform exponential smoothing

Our program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the data science with Python course covers everything you need to know, whether you’re a novice or an expert.

Yes, our applied Data Science with Python course is designed to offer flexibility for you to upskill as per your convenience. We have both weekday and weekend batches to accommodate your current job.

In addition to the training hours, we recommend spending about 2 hours every day, for the duration of course. This format is convenient when compared to other Data Science with Python courses.

The Data Science with Python course is ideal for:

  • Anyone Interested in the field of data science
  • Anyone looking for a more robust, structured Python learning program
  • Anyone looking to use Python for effective analysis of large datasets
  • Software or Data Engineers interested in quantitative analysis with Python
  • Data Analysts, Economists or Researcher

There are no prerequisites for attending this practical Data Science with Python certification course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

Below are the technical skills that you need if you want to become a data scientist.

  • Mathematics - You don't need to have a Ph.D. in math but it is important to have a basic knowledge of linear algebra, algorithms, and statistics.
  • Machine Learning – Stand out from other data scientists by learning ML techniques, such as logistic regression, decision trees, supervised machine learning, etc. These skills will help in solving different data science problems.
  • Coding – In order to analyze the data, the data scientist must know how to manipulate codes. Python is one of the most popular and easy languages.

Other important skills are

  • Software engineering skills (e.g. distributed computing, algorithms and data structures)
  • Data mining
  • Data cleaning and munging
  • Data visualization (e.g. ggplot and d3.js) and reporting techniques
  • Unstructured data techniques
  • R and/or SAS languages
  • SQL databases and database querying languages
  • Big data platforms like Hadoop, Hive, and Pig 
  • Proficiency in Deep Learning Frameworks: TensorFlow, Keras, Pytorch
  • Cloud tools like Amazon S3 

We have listed down all the essential Data Science Skills required for Data Science enthusiasts to start their career in Data Science

Apart from these Data Scientists are also required to have the following business skills:

  • Analytic Problem-Solving – In order to find a solution, it is important to first understand and analyze what the problem is. To do that, a clear perspective and awareness of the right strategies are needed.
  • Communication Skills – Communicating customer analytics or deep business to companies is one of the key responsibilities of data scientists.
  • Intellectual Curiosity -  If you are not curious enough to get an answer to that "why", then data science is not for you. It’s the combination of curiosity and thirst to deliver results that offers great value to a commercial enterprise.
  • Industry Knowledge – Last, but not least, this is perhaps one of the most important skills. Having solid industry knowledge will give you a more clear idea of what needs attention and what needs to be ignored. 

To attend the Data Science with Python training program, the basic hardware and software requirements are as mentioned below -

Hardware requirements

  • Windows 8 / Windows 10 OS, MAC OS >=10, Ubuntu >= 16 or latest version of other popular Linux flavors
  • 4 GB RAM
  • 10 GB of free space

Software Requirements

  • Web browser such as Google Chrome, Microsoft Edge, or Firefox

System Requirements

  • 32 or 64-bit Operating System
  • 8 GB of RAM

On adequately completing all aspects of the Data Science with Python course, you will be offered a Data Science with Python certification from KnowledgeHut. 

In addition, you will get to showcase your newly acquired data-handling and programming skills by working on live projects, thus, adding value to your portfolio. The assignments and module-level projects further enrich your learning experience. You also get the opportunity to practice your new knowledge and skillset on independent capstone projects.

By the end of the course, you will have the opportunity to work on a capstone project. The project is based on real-life scenarios and carried-out under the guidance of industry experts. You will go about it the same way you would execute a data science project in the real business world.

Below is the roadmap to becoming a data scientist:

  • Getting Started: Choose a programming language in which you are comfortable. We suggest Python as a suitable programming language.
  • Mathematics and Statistics: The science in Data Science is all about dealing with the data (maybe numerical, textual or an image), making patterns and relationships between them. You must have a good understanding of basic algebra and statistics.
  • Data Visualization: One of the most important steps in this learning path is the visualization of data. You must make it as simple as possible so that the other non-technical teams are able to grasp its contents as well. It is important to learn data visualization to communicate better with the end-users.
  • ML and Deep Learning: Having deep learning skills to go along with basic ML skills on the CV is a must for every data scientist as it is through deep learning and ML techniques that you will be able to analyze the data given to you. 

Data Science is one of the emerging fields in terms of its scope to business and job opportunities. Python is one of the most popular programming languages and has become the language of choice for Data Scientists. Learning Python with Data Science puts you in a favourable position to be hired as a skilled data scientist.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through our LMS.

Listen, learn, ask questions, and get all your doubts clarified from your instructor, who is an experienced Data Science and Machine Learning industry expert.

The Data Science with Python course is delivered by leading practitioners who bring trending, best practices, and case studies from their experience to the live, interactive training sessions. The instructors are industry-recognized experts with over 10 years of experience in Data Science. 

The instructors will not only impart conceptual knowledge but end-to-end mentorship too, with hands-on guidance on the real-world projects.

Our Date Science course focuses on engaging interaction. Most class time is dedicated to fun hands-on exercises, lively discussions, case studies and team collaboration, all facilitated by an instructor who is an industry expert. The focus is on developing immediately applicable skills to real-world problems.

Such a workshop structure enables us to deliver an applied learning experience. This reputable workshop structure has worked well with thousands of engineers, whom we have helped upskill, over the years. 

Our Data Science with Python workshops are currently held online. So, anyone with a stable internet, from anywhere across the world, can access the course and benefit from it.

Schedules for our upcoming workshops in Data Science with Python can be found here.

We currently use the Zoom platform for video conferencing. We will also be adding more integrations with Webex and Microsoft Teams. However, all the sessions and recordings will be available right from within our learning platform. Learners will not have to wait for any notifications or links or install any additional software.

You will receive a registration link from our LMS to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our platform and start your educational journey.

Yes, there are other participants who actively participate in the class. They remotely attend online training from office, home, or any place of their choosing.

In case of any queries, our support team is available to you 24/7 via the Help and Support section. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from our LMS at any time. At the beginning of every session, there will be a 10-12-minute recapitulation of the previous class.

Should you have any more questions, please raise a ticket or email us at and we will be happy to get back to you.

We at KnowledgeHut, conduct Data Science with Python courses in all the cities across the globe, and here are a few listed for your reference:



SydneyNoidaBaltimoreNew Jersey
TorontoPuneBostonNew York
OttawaKuala LumpurChicagoSan Diego
BangaloreSingaporeDallasSan Francisco
ChennaiCape TownFremontSan Jose
HyderabadArlingtonLos Angeles

Additional FAQs on the Data Science with Python Training

Careers in Data Science

In 2012, Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century. Companies like Google, Facebook and others collect user data and sell them to ad companies to earn profits. How do you think they know whether you like dogs or cats? How do you think Amazon knows what products to recommend to you even when they haven’t explicitly asked you about it? The answer is data. Some other major reasons why data science is popular are:
  • Data-driven decision making is increasing in demand.
  • Due to the lack of well-trained data scientists, professionals trained in data science are offered the highest salary in the tech world.
  • Data is being collected at an exceptionally high rate, which requires an equal rate of analysis Which are the global cities in which KnowledgeHut conducts Data Science with Python certification training? to make the most of it. Data scientists can help a company take crucial marketing decisions based on their findings from raw data. 
Therefore,  Data Science is in demand both from a company’s and from an employee’s perspective.

Data Science and Machine Learning go hand in hand. While Machine Learning is the ability of a machine to find patterns from data, Data Science is the mechanism by which the machines are provided with data. The more the availability of data, the more is the complexity and difficulty in compiling new predictive models that can accurately and efficiently work on this data. This is where the role of Machine Learning comes in, to leverage Data Science techniques and make sense of the large amounts of data, and to convert it into meaningful information.

Data Scientist Jobs

A data scientist is an individual who is responsible for discovering patterns and inferencing information from vast amounts of structured as well as unstructured data, in order to meet the business goals and needs.In this modern business scenario that is generating tons of data every day, the role of a Data Scientist is becoming all the more important. This is because the data generated is a gold mine of patterns and ideas that could prove to be very helpful in the advancement of a business. It is up to the data scientist to extract the relevant information and make sense of it in order to benefit the business.

Data Scientist Roles and Responsibilities:

  • Fetching data that is relevant to the business from among the huge amount of data that is available in the form of Structured as well as Unstructured Data.
  • Organize and analyze the data that is extracted from the piles of data.
  • Creation of Machine Learning techniques, programs, and tools in order to make sense of the data.
  • Perform statistical analysis for relevant data and predict future outcomes from it.

Data scientist has been declared as the hottest job of the 21st century. Due to high demand and less number of data scientists, data scientists earn base salaries up to 36% higher than other predictive analytics professionals. The salary of a data scientist depends on 2 things:

  • Type of company
    • Startups: Highest pay
    • Public: Medium pay
    • Governmental and Education sector: Lowest pay
  • Roles and responsibilities
    • Data scientist: ₹6,50,000/yr
    • Data analyst: ₹4,05,000/yr
    • Database Administrator: ₹6,48,987/yr

There are several career options for a data scientist –

  1. Data Scientist
  2. Data Architect
  3. Data Administrator
  4. Data Analyst
  5. Business Analyst
  6. Marketing Analyst
  7. Data/Analytics Manager
  8. Business Intelligence Manager

A Data Scientist is an individual who has the combined abilities of a mathematician, a computer scientist, and a trend spotter. The job of a Data Scientist is to decipher large volumes of data, mine the relevant parts of this data and then analyze this data so as to make predictions for similar data in the future. A career path in the field of Data Science can be explained in the following ways.

  • Business Intelligence Analyst: A Business Intelligence Analyst is an individual who has the job of figuring out the business as well as the market trends. This he/she does by the analysis of data in order to develop a clear picture of where exactly the business stands in the business environment.
  • Data Mining Engineer: A Data Mining Engineer is an individual who has the job of not only examining the data for the needs of the business, but also for the benefit of a third party. In addition to his job of the examination of data, a Data Mining Engineer also needs to create sophisticated algorithms that further aid in the analysis of data.
  • Data Architect: The role of a Data Architect is to work in tandem with system designers, developers and users in order to create blueprints that are used by data management systems in order to integrate, protect, maintain as well as centralize data sources.
  • Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses as well as the development of an understanding of data, so as to explore patterns from the given data. Data Scientists then move on to the development of algorithms and systems that make use of this data in a productive manner so as to further the interests of business.
  • Senior Data Scientist: A Senior Data Scientist is tasked with anticipating future business needs and shaping the projects, systems and data analyses of today to suit those business needs in the future.

If you are thinking to apply for a data science job, then follow the below steps to increase your chances of success:

  • Study: To prepare for an interview, cover all important topics, including-
    • Probability
    • Statistics
    • Statistical models
    • Machine Learning
    • Understanding of neural networks
  • Meetups and conferences: Tech meetups and data science conferences are the best way to start building your network or expanding your professional connections.
  • Competitions: Implement, test and keep polishing your skills by participating in online competitions like Kaggle.
  • Referral: According to a recent survey, referrals are the primary source of interviews in data science companies. So, make sure your LinkedIn profile is up to date.
  • Interview: If you think you are all equipped for the interviews, then go for it. Learn from the questions that you were not able to answer and study them for the next interviews.

Referrals are the most effective way to get hired. Some of the other ways to network with data scientists are:

  • Data science conferences
  • Online platforms such as LinkedIn and others
  • Social gatherings like Meetup

Due to high demand and low supply in case of data scientists in the industry, the expectations from them are also high. However, this means that the recognition and career benefits (like salary) are exceptionally high as well. If you are aspiring to be a data scientist then we have compiled key points, which the employers generally look for in data scientists while hiring:

  • Education: Most of the data scientists are Masters and PhDs in the field so it is essential that you acquire higher education if you aim to be a data scientist. Getting certified also adds to it.
  • Programming: Data science is a field of computer science in general so it goes without question that your programming skills determine how well you can handle the job.
  • Libraries/Tools: Programming languages are a basic platform upon which there are libraries and tools built which in turn help you in preparing, analysis, as well as visualization of data.
  • Machine Learning: After preparing the data, deep learning is to be applied to it to analyze the patterns and find a relationship in it. Having ML skills is a must.
  • Projects: Projects help provide proof of your skills and they help to determine your strong points and interests which in turn helps you to explore this field as well.
  • Communication: Data scientist communicates not only within their own team of data scientists but with other non-tech people such as Sales team, marketing team etc. who do not understand technical language. It is, therefore, imperative that a data scientist is able to explain his/her findings in a simple way.

We have compiled the key points, which the employers generally look for while hiring data scientists:

  • Education: Data scientists have more PhDs than any of the other job titles. So, getting a degree will be beneficial. Getting certified also adds to it.
  • Programming: Python is a great programming language for data scientists. So, it is important to learn Python Basics before you start learning any data science libraries.
  • Machine Learning: After preparing the data, deep learning is used to analyze the patterns and find a relationship. Having ML skills is a must.
  • Projects: The best approach to learn data science is by practising with real-world projects so that you can build your portfolio.

Data Science with Python

  • Python is a multi paradigm programming language - this means that the various facets of Python are most suited for the field of Data Science. It is a structured and object oriented programming language that contains several libraries and packages that are useful for the purposes of Data Science.
  • The inherent simplicity and readability of Python as a programming language makes it a language that is preferred by data scientists. The huge number of dedicated analytical libraries and packages that are tailor made for use in data science are some of the main reasons why data scientists prefer the use of Python for Data Science projects, as opposed to any other programming language.
  • Another great thing about Python which makes it the language of choice for data scientists is the broad and diverse range of resources that are available at the disposal of a data scientist, should he/she get stuck at a particular point or problem while developing a Python program or model for Data Science.
  • The vast Python community is another big advantage that Python has over other programming languages. Since there are millions of developers working on the same problems with the same programming language every day, it is very easy for a developer to get help in resolving his/her problems because the chances are that someone else had been stuck at the same problem in the past and its resolution has already been found. If no one else has encountered a similar problem, the Python community is quite helpful and tries its best to help their fellow Data Science in Python developers.

There are many factors that make a program a success. Like every other educational field, the advancement in Data Science also depends on multiple reasons.

  • Starting with the very basic question, are you a beginner, an intermediate learner or someone with deep prior knowledge i.e. an expert? If you’re a beginner who joins an expert program, everything will go over your head. And if you’re an expert, joining a beginner’s class would feel like a waste of time and money since you’re probably aware of whatever that’s being taught.
  • Once you know what your current level is, the next question is what kind of learner are you? Whether you prefer the traditional classroom coaching where you follow a certain schedule with a specific timing or you prefer the independent style that the online coaching offers.
  • Again,one of the most important factors is money and time. Since there are endless options, you must decide which one you want according to your needs.
  • Always remember to check the reviews or talk to current or ex-students of the program, they will help you understand how the program can really help.
  • Also, before joining a full-fledged program, make sure to try a free course. It will help you firm your decision whether you are really into data science or not.

Data Science deals with identification, representation, and extraction of meaningful information, so any programming language equipped with tools to do these tasks efficiently will be naturally popular. Python is one such popular language and the reasons for the same include:

  • Short learning curve: Unlike its competitor, R, Python is comparatively easy and quick to learn due to its readable and easy-to-understand syntax.
  • Scalability: YouTube migrated to Python due to its efficient scaling capabilities. As compared to its competitors - R, MATLAB etc., Python has a significant lead in scalability due to the flexibility it provides during problem-solving.
  • Libraries: Python is the leading language for machine learning projects due to the packages it offers to the developers. Packages like pandas, scikit-learn, etc. allow for ML algorithms to be applied to the data easily.
  • Data visualization: With the help of matplotlib, Python enables us to plot complex data representations into 2D plots. Data visualization is a significant process in the job of a data scientist. With the help of Seaborn, ggplot etc. along with matplotlib, Python provides us with a great data visualization tool.

As data science is a huge field and involves multiple libraries to work together in a smooth way, it is essential that you choose an appropriate programming language.

  • R: Although it has a steep learning curve, it has various advantages.
    • The big open-source community that provides R with high-quality open source packages.
    • Includes loads of statistical functions and handles matrix operations smoothly.
    • Via ggplot2, R provides us with a great data visualization tool.
  • Python: Though it has fewer packages than R, Python is still one of the most sought after languages in the data science field.
    • Pandas, scikit-learn, and tensorflow provide with most of the libraries needed for data science purposes.
    • Easy to learn and implement it.
    • It has a big open-source community as well.
  • SQL: SQL is a structured query language which works upon relational databases.
    • Pretty readable syntax.
    • Efficient at updating, manipulating and querying data in relational databases.
  • Java: Even though it has less number of libraries for data science purposes and with Java’s verbosity limiting its potential, it has many advantages as well:
    • Compatibility. Systems are already coded in Java in the backend, and its therefore easier to integrate Java data science projects to it.
    • It is a high-performance, general purpose, and a compiled language.
  • Scala: Scala runs on JVM and has a complex syntax. Still, it is a preferred language in data science domain due to the following advantages:
    • As it runs on JVM, any Scala program can run on Java as well.
    • When used along with Apache Spark, we get high-performance cluster computing.

Follow these steps to successfully install Python 3 on windows:

  • Download and setup: Go to the download page and setup your python on your windows via GUI installer. While installing, select the checkbox at the bottom asking you to add Python 3.x to PATH, which is your classpath and will allow you to use Python’s functionalities from terminal.

Alternatively, you can also install Python via Anaconda as well. Check if Python is installed by running the following command, you will be shown the version installed:

python --version

  • Update and install setuptools and pip: Use below command to install and update 2 of most crucial libraries (3rd party):

python -m pip install -U pip

Note: You can install virtualenv to create isolated Python environments and pipenv, which is a Python dependency manager.

You can simply install Python 3 from their official website through a .dmg package, but we recommend using Homebrew to install Python as well as its dependencies. To install Python 3 on Mac OS X, just follow the below steps:

  1. Install xcode: To install brew, you need Apple’s Xcode package, so start with the following command and follow through it:$ xcode-select --install

  2. Install brew: Install Homebrew, a package manager for Apple, using following command:/usr/bin/ruby -e "$(curl -fsSL)"Confirm if it is installed by typing: brew doctor

  3. Install Python 3: To install latest version of python, use:

brew install python

  1. To confirm its version, use: python --version

You should also install virtualenv, which will help you create isolated places to run different projects and may run even on different python versions.

Follow the below steps to successfully install Python 2 on your windows:

  1. Download the MSI file from the official download website and go through its GUI setup.
  2. Suppose you have installed Python 2.x, so windows would create a folder 

C:\Python2x This helps in installing multiple versions of Python on your windows machine.

  1. To use python command line from terminal, go to Control Panel > System > Advanced system settings > Environment variables. Add C:\Python2x; (with semicolon) to the PATH variable value and click OK.
  2. Restart the command prompt and type the following to see the installed python version: Python --version

Unstructured data refers to the undefined contents of a data set that cannot be fit into structured database tables. It is basically information that is not organized in a predefined manner nor has a data model that is pre-defined. Unstructured data is generally text-heavy but may also consist of other data such as numbers, facts, figures, audio, video etc.

While unstructured data may be difficult to organize, if a company is able to tap into it in a meaningful and efficient manner, it is like digging up a bag of gold.Unstructured data can aid companies in the formation of important business decisions if a company is able to integrate this unstructured data into their information management systems and landscapes.

Pandas and NumPy are two of the most used Python libraries for data manipulation. Most of the times they are used in a single project. Although Pandas is a library build directly off from NumPy, there are some differences between both of them.




Data input

Tabular form - CSV or SQL formats

Numerical data

Main feature

Helps add, edit, or create columns or rows to the table.

Helps perform multiple operations on Array.

Building block

Series which is built off from ndArrays of NumPy.

ndArrays - Allow mathematical operations to be vectorized and when compared to Python lists, they are stored with much better efficiency.

Ways to access data

We can use labeled data - integers as well as numbers to label the elements of the series object.

Only integers are used for labeling the elements.

What Learners Are Saying

Ong Chu Feng Data Analyst
The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, Knowledgehut!

Attended Data Science with Python Certification workshop in January 2020

Anubhav Ingole Senior Data Scientist

At KnowledgeHut, I had one of my best educational experiences. The course is extensive and contains many materials, including videos, PPTs, and PDFs. In addition, all the trainers and the support staff were incredibly accommodating and accessible.

Attended Data Science with Python Certification workshop in August 2022

Akshay patole User

This 2-day training session helped me widen my knowledge of Scrum methodologies and Agile principles. Everything was well-organized, though it was an online session. My trainer explained the concepts with real-life examples and ensured every participant was on the same page. I highly recommend this course to everyone who wants to become a Certified Scrum Master. Kudos to the team efforts behind this!

Attended Certified ScrumMaster (CSM)® workshop in August 2022

Madeline R Front-End Developer

I know from first-hand experience that you can go from zero and just get a grasp on everything as you go and start building right away. 

Attended Full-Stack Development Bootcamp workshop in July 2022

Steffen Grigoletto Senior Database Administrator

Everything was well organized. I would definitely refer their courses to my peers as well. The customer support was very interactive. As a small suggestion to the trainer, it will be better if we have discussions in the end like Q&A sessions.

Attended PMP® Certification workshop in April 2020

Yancey Rosenkrantz Senior Network System Administrator

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Attended Agile and Scrum workshop in April 2020

Meg Gomes casseres Database Administrator.

The Trainer at KnowledgeHut made sure to address all my doubts clearly. I was really impressed with the training and I was able to learn a lot of new things. I would certainly recommend it to my team.

Attended PMP® Certification workshop in January 2020

Tilly Grigoletto Solutions Architect.

I really enjoyed the training session and am extremely satisfied. All my doubts on the topics were cleared with live examples. KnowledgeHut has got the best trainers in the education industry. Overall the session was a great experience.

Attended Agile and Scrum workshop in February 2020


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