Data Science with Python Training in Houston, TX, United States

Get hands-on 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
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
  • 100 + Countries and counting

Grow your Data Science skills

This comprehensive hands-on course takes you from the fundamentals of Data Science to an advanced level in weeks. 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.

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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 this 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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Invest in forward-thinking data talent to leverage data’s predictive power, craft smart business strategies, and drive informed decision-making.

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Data Science with Python

What is Data Science

In 2012, Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century. Companies like Google, Facebook collect user data and sell them to ad companies to earn crazy 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 to make the most of it. Data scientists can help a company take crucial marketing decisions based on their findings from raw data. 

Therefore, it’s in demand both from a company’s perspective and from an employee’s perspective

In Houston, colleges like The University of Texas, University of Houston, and Sam Houston State University offers online and offline courses in Data Science that can help you learn the skills required to be a top-notch Data Scientist. The top skills that are needed to become a data scientist include the following:

  1. Python Coding: Python is the most popular programming language used in Data Science. It is simple, versatile, can take different data formats and aid in the processing of data. It also helps in creating datasets and performing operations on them.
  2. R Programming: If you want to become a data scientist, you need to have knowledge of an analytical tool. This is where the R language comes into play. It is a go-to programming language if you want to make your data science problems easier to solve.
  3. Hadoop Platform: Although not a must, in-depth knowledge of the Hadoop platform is recommended as it is used in several data science projects. A study recently revealed that Hadoop is one of the leading skill requirements for a job as a data scientist.
  4. SQL database and coding: SQL is a database language used for accessing, working, and communicating the data. With this, a data scientist can gain insights into the formation and structure of the database. MySQL is another such language with concise commands through which you can perform operations using less technical skills in less time.
  5. Machine Learning and Artificial Intelligence: It is a prerequisite to be in the field of data science. Knowledge of Machine Learning and Artificial Intelligence will help you analyze the data and use this data to gain insights. You should be familiar with topics like a neural network, reinforced learning, logistic regression, machine learning algorithms, decision, tress, adversarial learning, etc. 
  6. Apache Spark: Apache Spark is a popular data sharing technology used for big computation. It is quite similar to Hadoop, except it is faster than Hadoop. This is because Sparks use system memory to cache its computation whereas Hadoop reads and writes to the disk. Therefore, it can be used to run the algorithms faster. It is a great framework while working with large datasets and handling complex unstructured data. Some other benefits of using Apache Spark include prevention of data loss, faster speed, and ease of carrying out operations.
  7. Data Visualization: It is the responsibility of a data scientist to visualize the results obtained after a series of complicated processes performed on the data in a format easily understandable by everyone. There are tools available to help with a visualization like ggplot, d3.js, matplotlib, and Tableau. It also helps in getting a quick insight and enabling into the new outcome.
  8. Unstructured data: Most of the data that we have is unstructured, unlabelled and cannot be organized into database values. Some examples of these unstructured data include blog posts, audios, videos, social media posts, customer reviews, etc.

If you want to become a successful Data Science professional, you need to incorporate these 5 essential behavioral traits in yourself:

  • Curiosity – If you want to be able to deal with such a huge amount of data, you must be curious and have an undying thirst for knowledge.
  • Clarity – If you are constantly looking for clarity by asking questions like ‘why’ or ‘so what’, data science is the field for you. You should know what you are doing and when you are doing, whether you are writing code or cleaning up data.
  • Creativity – A data scientist needs creativity for developing new modeling features, creating new tools and developing new ways for data visualization.
  • Skepticism – Skepticism is as important as creativity for the job of a data scientist. It is required to stay in the real world and not get carried away with creativity.

In Houston, TX, leading companies are looking for Data Scientists to help in optimizing their business including CGG, Tessella, BBVA Compass, Harris Health System, Hewlett Packard Enterprise, KPMG, Microsoft, Two Sigma Investments, LLC., GSI Environmental, Cardno. Etc. The 5 proven benefits of being a data scientist in Houston are:

  1. High Pay: Since there is a high demand of data scientists right now and the number of experienced data scientists is low, data scientist jobs have become one of the highest paying jobs in the tech world.
  2. Good bonuses: With the handsome pay, the job of a data scientist comes with signing perks, equity shares, and impressive bonuses.
  3. Education: To become a data scientist, you need to earn a Master's degree or a Ph.D. that opens up doors to work as a researcher or lecturer in government or a private institution.
  4. Mobility: A job of a data scientist can increase your standard of living as most of the organizations that collect data are located in developed countries.
  5. Network: Being a data scientist will involve publishing research papers, attending conferences, tech talks, and meetups that will help you network. This will help you for referral purposes in the future.

Data Scientist Skills & Qualifications

If you want to become a data scientist, you must have these 4 business skills: 

  1. Analytic Problem-Solving – The first step to finding a solution to a problem is to understand and analyze the problem. You need to have a clear perspective to develop the right strategies required to solve the problem.
  2. Communication Skills – Communicating skills are very important as it helps the data scientists explain deep business and customer analytics to the companies.
  3. Intellectual Curiosity: To be a good data scientist, you need to be curious and have a thirst to produce results that will boost the value of your commercial enterprise.
  4. Industry Knowledge – A good data scientist has a solid knowledge of the industry he/she is working in. This will help you in analyzing the data as you will know what is important and what is not.

Before you get a job as a data scientist, you need to brush up on your data science skills. Here are the 5 best ways to do it:

  • Boot camps: Lasting for about 4 to 5 days, boot camps are a great way to brush up your basics. They help you get theoretical knowledge as well as practical hands-on experience.
  • MOOC courses: MOOC are the online courses taught by data science experts that help you stay updated with the latest trends in the industry and polish your implementation skills through multiple assignments.
  • Certifications: With a certification, you will have improved your CV significantly and added an additional skill set. 
  • Projects: When it comes to brushing up your skills, projects are the best way to do it. The more you work, the more refined your skills will be. You can either work on an existing project or take on a new one.
  • Competitions: You can participate in online competitions like Kaggle that can improve your problem-solving skills. During the competition, you will have to find a solution to a problem with certain restraints and satisfy all the requirements.

In Houston, TX, all the major corporations are looking to harness the benefits of Data. The employers looking for Data Scientists include Harris Health System, Amazon Web Services, Two Sigma Investments, CGG, Tessella, BBVA Compass, Hewlett Packard Enterprise, KPMG, Microsoft, LLC., GSI Environmental, Cardno., ExxonMobil, Schlumberger, TGS, McDermott, Pros., Noble Energy, Inc, David Weekley Homes, Drillinginfo, etc. 

The best way to practice your data science skills is by solving data science problems, for which there are several problems available online. Here we have listed a few of them,categorized according to their difficulty level and your expertise level.

  • Beginner Level
    • Iris Data Set: The Iris Data Set contains 4 columns and 50 rows which are perfect for a beginner. It is a popular, resourceful, easy, and versatile dataset that uses pattern recognition. With this data, you will be able to learn the different classification techniques and start your journey in the Data Science field.Practice Problem: The problem is to predict the flower’s class using these parameters.
    • Bigmart Sales Data Set: The Retail sector is an industry that uses analytics for optimizing their business processes. While solving the problem, you will deal with retail concepts like product bundling, inventory management, customizations, etc. All of these can be handled using business analytics and data science. It is a regression problem consisting of 12 columns and 8523 rows.Practice Problem: The problem is to predict the total sales of the retail store.
  • Intermediate Level:
    • Black Friday Data Set: This dataset consists of sales transactions made in a retail store. This dataset is the best as it helps you explore your engineering skills while giving you an understanding of how millions of customers shop daily. It is a regression problem with 12 columns and 550,069 rows.Practice Problem: The problem is to predict the total purchase amount.
    • Text Mining Data Set: The Text Mining Data Set contains aviation safety reports describing the issues encountered during certain flights. This data set was obtained in 2007 during the Siam Text Mining Competition. It is a high-dimensional and multi-classification problem containing 30,348 rows and 21,519 columns.Practice Problem: The problem is the classification of the documents on the basis of their labels.

  • Advanced Level:
    • Identify the digits data set: Comprising of 7000 images with dimensions of 28X28 each, this dataset involves studying, analyzing and recognizing different elements present in an image.Practice Problem: The problem is the identification of the elements present in the image.
    • Vox Celebrity Data Set: This large scale identification problem is very important in the arena of deep learning using audio processing. The dataset contains 100,000 words spoken by 1,251 celebrities extracted from YouTube videos. It can help you understand the process of isolating and identifying speech.Practice Problem: The problem is to identify the voice of the celebrity.

How to Become a Data Scientist in Houston, Texas

If you want to become a top-notch data scientist, you need to follow the below mentioned steps:

  1. Getting started: The first step is to select a programming language to work with. We recommend that you pick either Python or R as they are the most popular languages used in the field of Data Science.
  2. Mathematics and statistics: A good data scientist must have a good grasp of basic algebra and statistics. You will need them while dealing with data, discovering patterns and relationships.
  3. Data visualization: Learning to visualize the data is an important step in becoming a data scientist. You need it for better communication with the end users and in helping the non-technical members of the team understand the content as well.
  4. ML and Deep learning: Every data scientist must be an expert in Deep Learning as well as Machine Learning. It helps you to analyze the data.

Here, we have compiled a list of steps required to become a Data Scientist:

  1. Degree/certificate: To be a data scientist, you need to have a degree in Data Science. You need to get started with a course that covers all the fundamentals. This course can be online or offline, depending on what suits you. During the course, you will be learning the application of cutting-edge tools. This is a tough job that demands continuous learning due to the rapid advancements in the field. You can also try getting certifications that will improve your CV significantly.
  2. Unstructured data: Tons of data is generated every day. Most of this data is in an unstructured format. It is the job of a data scientist to deal with this unstructured data and discover patterns in it. This makes the job more complex as a lot of work is required to structure the data.
  3. Software and Frameworks: Frameworks play an essential role in data science. When used with a programming language like Python or R, they help in structuring the data and analyzing it. 
    • R language has a steep learning curve. Still, it is one of the most used programming languages in Data Science. They have a lot of statistical functions that help in data analysis. About 43% of Data Scientist perform their data analysis using R.
    • Hadoop is a framework that is used by data scientists in situations where, compared to memory at hand, the available data is in excess. The framework conveys the data to different points on the machine. Spark is another popular framework. Used for computational purposes, Spark is faster than Hadoop. It can also prevent data loss.
    • Once you have mastered the programming languages and the framework, you can move on to the databases. A good data scientist must have an in-depth knowledge of SQL queries.
  4. Machine learning and Deep Learning: Once the data has been collected and prepared, you need to apply machine learning and deep learning algorithms to analyze the data. The Data Science models are trained to deal with the data that is provided.
  5. Data visualization: Data visualization is a very important skill for a data scientist. It helps them make an informed business decision after carefully analyzing the data. The data must be presented in the form of charts and graphs. There are several visualization tools available for this purpose including ggplot2, matplotlib, etc.

Getting a degree in Data Science is very important to help land a job as a Data Scientist. About 88% of data scientists have a Master's degree and 46% have PhDs. There are many universities in Houston offering Data science courses, including Sam Houston State University, University of Houston, The University of Texas, etc. The reasons why it is so important include:

A degree is very important because of the following – 

  • Networking – When you are pursuing a degree, you can start building your network by making friends and acquaintances. It will help you a lot later.
  • Structured learning – When you are enrolled in a course, you have to follow a schedule and keep up with the curriculum. This structured learning is effective and beneficial.
  • Internships – During your internship, you will get practical hands-on experience.
  • Recognized academic qualifications for your résumé – A degree from a prestigious institution will look good on your CV and help you land a better job.

You can grade yourself on the scorecard below and determine if you should go for a Master's degree or not. If your total score is more than 6 points, we recommend a Master's degree:

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • You have a weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma): 2 points
  • You are from a non-STEM background: 5 points
  • You have less than 1 year of experience in working with Python programming language: 3 points
  • You have never been part of a job that requires you to code on a regular basis: 3 points
  • You think you are not good at independent learning: 4 points
  • You do not understand when we tell you that this scorecard is a regression algorithm: 1 point

Programming is the most basic and important skill that you can have as a data scientist. Here is why programming knowledge is a must to become a data scientist:

  • Data sets: While working in the field of data science, you will have to deal with large datasets. To analyze this huge amount of data, you will need the help of programming.
  • Statistics: Knowledge of statistics will be of no use if the data scientist doesn’t know how to program to implement it.
  • Framework: With the knowledge of programming, a data scientist will be able to build a framework that can analyze experiments automatically, manage the data pipeline and visualize data.

Data Scientist Jobs in Houston, Texas

If you want to get a job as a data scientist, you need to follow the given logical sequence of steps:

  1. Getting started: First things first, you need to select a programming language that can be used in Data Science and you are comfortable working in. The most preferred languages by data scientists are Python and R. You also need to understand what are the roles and responsibilities of a data scientist.
  2. Mathematics: If you want to make sense of raw data, decipher patterns and find relationships, you need to have a good command over mathematics and statistics. You need to pay special attention to a few topics like:
    • Descriptive statistics
    • Inferential statistics
    • Linear algebra 
    • Probability
  3. Libraries: Data Science includes processes like preprocessing the data, plotting the structured data, applying machine learning algorithms to this data. To accomplish these tasks, libraries are required. Some of the famous libraries are mentioned below:
    • Ggplot2
    • Matplotlib
    • NumPy
    • Pandas
    • Scikit-learn
    • SciPy
  4. Data visualization: It is the responsibility of a data scientist to make the data as simple as possible so that the non-technical members of the team can understand it as well. You can try creating graphs and charts. To accomplish this, there are certain tools available:
    • Ggplot2 - R
    • Matplotlib - Python
  5. Data preprocessing: Most of the data that we have is in unstructured form. To make it ready for analysis, a data scientist has to preprocess it. This is done using variable selection and feature engineering. Once the preprocessing is done, the data is injected into the machine learning tool for analysis.
  6. ML and Deep learning: For a data scientist, proficiency in deep learning and machine learning is important. Deep learning is essential while dealing with huge data sets while machine learning is required for data analysis. You need to be well versed with tops like CNN, RNN, and Neural networks.
  7. Natural Language processing: Proficiency in Natural Language Processing is important because it involves data classification and processing of textual data.
  8. Polishing skills: If you want to polish and exhibit your data science skills, you can go for online competitions like Kaggle. Apart from this, you can create your own projects and explore the field of data science.

While preparing for the job of a data scientist, here are the 5 important steps that you need to follow:

  • Study: You need to cover all the important topics while preparing for the interview. Here are some important topics:
    • Machine Learning 
    • Probability
    • Statistics
    • Statistical models
    • Understanding neural networks
  • Meetups and conferences: Go build your professional network and expand your connections by meeting other data science professionals in tech meetups and conferences.
  • Competitions: Participate in online competitions like Kaggle for implementing, testing, and polishing your skills. 
  • Referral: Update your LinkedIn profile and find someone who can refer you. Referrals are the primary source of interviews in the tech world and will help you land the job.
  • Interview: If you think you are ready, go for the interview. You might have to go through a couple of bad interviews before you get a job. Just learn from each interview and get answers to questions you couldn’t answer in the interview.

The main responsibility of a data scientist is to analyze the data to decipher patterns and relationships and use this information to meet the needs and goals of the business. This data is available in the raw form, which can be unstructured as well as structured.

With tons of data generated every minute, the job of a data scientist has become more important than ever. This data is a goldmine of information that can help in the advancement of a business. It is up to the data scientist to extract the insights from the huge pile of data and benefit the business. The roles and responsibilities of a data scientist include:

Data Scientist Roles & Responsibilities:

  • Getting the relevant data from the huge pile of structured and unstructured data provided to them by the organization.
  • Organizing and analyzing the data.
  • To make sense of the data, creating machine learning techniques, tools, and programs.
  • Performing statistical functions on relevant data for predicting future outcomes.

Because of high demand and less number of data scientist’s issue, there has been an increase in a 36% increase in base salaries of data scientists that is significantly higher than any other predictive analytics professionals. The pay of a data scientist depends on the following two things:

  • Type of company
    • Governmental & Education sector: Lowest pay 
    • Public: Medium pay 
    • Startups: Highest pay 
  • Roles and responsibilities
    • Data analyst: $55,125/yr
    • Database Administrator: $80,111/yr
    • Data scientist: $123,086/yr

To be a successful data scientist, one must be skilled in Mathematics, computer science, and trend spotting. It is the responsibility of a data scientist to analyze the large volumes of data to make predictions for the future. The career path of a data scientist is as follows:

Business Intelligence Analyst: To figure out the needs of the business and market trends, a business intelligence analyst is required. To develop a clear picture of the current standing of the business in the business environment, analysis is done as a part of this job.

Data Mining Engineer: A Data Mining Engineer is responsible for the examination of data required to fulfill the needs of the business. They might be hired by the company as a full-time employee or a third party. Apart from examining the data, the job of a Data Mining Engineer also involves the creation of a sophisticated algorithm that helps in further analysis of data.

Data Architect: Data Architects work alongside System developers, designers, and users for creating blueprints. These blueprints are then used by the data management system that integrates, centralizes, maintain and protect the data sources.

Data Scientist: The job of a Data Scientist is to analyze the business case, develop a hypothesis and an understanding of data. They are also responsible for developing systems and algorithms that use this data in a productive manner to further the interests of the business.

There are several professional groups and associations created for data scientists for networking and discussing data science including:

  • Houston Data Science
  • Flatiron School Houston
  • North Houston Data Analytics and Machine Learning
  • Open Source Data Science
  • Data Natives Houston

To network with other data scientists in Houston, TX to potentially fill data scientist employees in a team, you can try visiting one of the following:

  • An online platform like LinkedIn
  • Social gatherings like Meetup 
  • Data science conference

The top 8 Data Science Career opportunities in Houston in 2019 are– 

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

To get a job as a data scientist, you need to mastery over some tools and software including the following:

  • Education: Data Scientists is one of the jobs that require you to have a Ph.D. A degree from a prestigious institution will not only look good on your CV but will help you get comprehensive knowledge required to manage and analyze unstructured data. You can also try getting certifications that will add to your skills. 
  • Programming: Being proficient in programming is a must to be a data scientist. You need to cover your basics before moving on to any data science library.
  • Machine Learning: Having deep learning and machine learning skills is a must to analyze the pattern and find a relationship.
  • Projects: The best way to learn data science to take on real-world data science projects that will also build your portfolio.

Data Science with Python Houston, Texas

  • Being a multi-paradigm programming language makes Python one of the most common and popular languages used by data scientists. It has multiple facets that help the data scientists in their projects. This structured, object-oriented programming language has several libraries and packages useful for data science purposes.
  • Python is simple and readable. This makes it the most preferred programming language used by the data scientists. It comes with customized packages and libraries perfectly suitable for the field of data science.
  • If you ever get stuck in a python code or while building a data science model using python, there is a broad and diverse range of resources that can help you get out of it. All these resources are available at the disposal of a data scientist.
  • Using python comes with a big advantage, the support of the vast python community. Python is a popular language used by millions of developers worldwide. So, if you get stuck somewhere, there is a huge chance that someone has been stuck there before and found a solution for it. And if your problem is new, the helpful python community will try to find a solution for you.

When it comes to data science, choosing an appropriate language that is fit for the field and you are comfortable working in is important. It is a huge field and you need multiple libraries to carry out the work in a smooth way. Here are the 5 most popular languages used by the data scientists worldwide:

  • R: R is considered a difficult language because of its steep learning curve. However, it comes with certain advantages that make it one of the most used programming languages in Data Science:
    • Comes with several statistical functions that aid in data analysis.
    • It can handle matrix operations smoothly.
    • With ggplot2, R acts as a great tool for data visualization.
    • There is a big open source R community that offers several open source packages.
  • Python: Python is the most commonly used and preferred language in the field of data science. Even though it offers less number of packages than R, the following advantages cover for it:
    • Python is easy to learn and implement.
    • Pandas, scikit-learn, and tensorflow cover most of the libraries needed by data scientists
    • There is a big, open-source community for Python as well.
  • SQL: SQL is a Structured Query Language that works on relational databases.
    • The syntax of the language is pretty easy to read.
    • Updating, querying, and manipulating data is very easy using SQL in relational databases.
  • Java: Java offers fewer libraries than other programming languages and its verbosity limits its potential. Still, it is used in many data science projects due to the following reasons:
    • It is a high-performance, compiled, and general purpose language.
    • There are systems with backend code in Java. So, it is easy to integrate java data science projects to it.
  • Scala: Even though it has a complex syntax, it is a preferred language by data scientists due to the following reasons:
    • It runs on JVM. That makes it compatible with Java as well.
    • If used with Apache Spark, you can get high-performance cluster computing.

If you want to download and install Python 3 on Windows, you need to follow these steps:

  • Download and setup: First, visit the download page and through the GUI installer, start installing python on the windows. While installing, make sure that select the checkbox that asks you to add Python3.x to PATH, the classpath. This will allow using the functionalities of python directly from the terminal.

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
Take your advanced statistics and predictive modelling skills to the next level in this advanced 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 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

The program is designed to suit all levels of Data Science expertise. From the fundamentals to the advanced concepts in Data Science, the course covers everything you need to know, whether you’re a novice or an expert. To facilitate development of immediately applicable skills, the training adopts an applied learning approach with instructor-led training, hands-on exercises, projects, and activities.

Yes, our 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.

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 course, however prior knowledge of elementary programming, preferably using Python, would prove to be handy.

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 course completion certificate 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.

Data Science with Python Workshop

The Data Science with Python workshop at KnowledgeHut is delivered through PRISM, our immersive learning experience platform, via live and interactive instructor-led training sessions.

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 PRISM to your e-mail id. You will have to visit the link and set your password. After which, you can log in to our Immersive Learning Experience 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 on PRISM. You can also reach out to your workshop manager via group messenger.

If you miss a class, you can access the class recordings from PRISM 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.

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

Ben Johnson Developer

The Backend boot camp is a great, beginner-friendly program! I started from zero knowledge and learnt everything through the learn-by-doing method. 

Attended Front-End Development Bootcamp workshop in July 2021

Zach B Front-End Developer

The syllabus and the curriculum gave me all I required and the learn-by-doing approach all through the boot camp was without a doubt a work-like experience! 

Attended Full-Stack Development Bootcamp workshop in May 2021

Ben Johnson Developer

The FSD boot camp is a great, beginner-friendly program! I started from zero knowledge and learnt everything through the learn-by-doing method. 

Attended Full-Stack Development Bootcamp workshop in April 2021

Madeline R 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 Back-End Development Bootcamp workshop in April 2021

Tyler Wilson Full-Stack Expert

The learning system set up everything for me. I wound up working on projects I've never done and never figured I could. 

Attended Front-End Development Bootcamp workshop in April 2021

Matteo Vanderlaan System Architect

I was totally impressed by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. The training session gave me the confidence to do better in my job.

Attended Certified ScrumMaster (CSM)® workshop in January 2020

Marta Fitts Network Engineer

The workshop was practical with lots of hands on examples which has given me the confidence to do better in my job. I learned many things in that session with live examples. The study materials are relevant and easy to understand and have been a really good support. I also liked the way the customer support team addressed every issue.

Attended PMP® Certification workshop in May 2020

Career Accelerator Bootcamps

Full-Stack Development Bootcamp
  • 80 Hours of Live and Interactive Sessions by Industry Experts
  • Immersive Learning with Guided Hands-On Exercises (Cloud Labs)
  • 132 Hrs
  • 4.5
Front-End Development Bootcamp
  • 30 Hours of Live and Interactive Sessions by Industry Experts
  • Immersive Learning with Guided Hands-On Exercises (Cloud Labs)
  • 4.5

Data Science with Python Certification Course in Houston, TX

A city that has launched a thousand rockets, perhaps Houston?s greatest claim to fame is the presence of the NASA space centre. But there is lot more to this dynamic city than high-powered government offices and institutions. Home to some of the greatest museums, art deco architecture buildings, shopping districts and culinary delights, the city has something for everyone. Also present here is the Texas Medical Center which has the world's largest concentration of healthcare and research institutions and several trade, mining, engineering, and international companies. Consistently ranked as among the best places in the U.S to do business, Houston is a great place to study and work in. You can chose among several of KnowledgeHut?s courses to start your career here. These courses are globally recognized and will help you get the right footing. Courses include PRINCE2, PMP, PMI-ACP, CSM, CEH, Big Data, Hadoop, Python, Data Analysis, Android Development and much more. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

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