Conditions Apply

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

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 42 hours of Instructor led Training
  • Interactive Statistical Learning with advanced Excel
  • Comprehensive Hands-on with Python
  • Covers Advanced Statistics and Predictive Modeling
  • Learn Supervised and Unsupervised Machine Learning Algorithms

Online Classroom (Weekend)

Apr 04 - May 17 08:00 AM - 11:00 AM ( CDT )

USD 2199

USD 649

Online Classroom (Weekend)

Apr 04 - May 09 09:00 AM - 01:00 PM ( CDT )

USD 2199

USD 649

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents


Rapid technological advances in Data Science have been reshaping global businesses and putting performances on overdrive. As yet, companies are able to capture only a fraction of the potential locked in data, and data scientists who are able to reimagine business models by working with Python are in great demand.

Python is one of the most popular programming languages for high level data processing, due to its simple syntax, easy readability, and easy comprehension. Python’s learning curve is low, and due to its many data structures, classes, nested functions and iterators, besides the extensive libraries, this language is the first choice of data scientists for analyzing, extracting information and making informed business decisions through big data.

This Data Science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.

Extensive hands-on labs and interview prep will help you land lucrative jobs.

What You Will Learn


There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.

3 Months FREE Access to all our E-learning courses when you buy any course with us

Who should Attend?

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

KnowledgeHut Experience

Instructor-led Live Classroom

Interact with instructors in real-time— listen, learn, question and apply. Our instructors are industry experts and deliver hands-on learning.

Curriculum Designed by Experts

Our courseware is always current and updated with the latest tech advancements. Stay globally relevant and empower yourself with the training.

Learn through Doing

Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.

Mentored by Industry Leaders

Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.

Advance from the Basics

Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.

Code Reviews by Professionals

Get reviews and feedback on your final projects from professional developers.


Learning Objectives:

Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in  data science.

Topics Covered:

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

Hands-on:  No hands-on

Learning Objectives:

In this module you will learn how to install Python distribution - Anaconda,  basic data types, strings & regular expressions, data structures and loops and control statements that are used in Python. You will write user-defined functions in Python and learn about Lambda function and the object oriented way of writing classes & objects. Also learn how to import datasets into Python, how to write output into files from Python, manipulate & analyze data using Pandas library and generate insights from your data. You will learn to use various magnificent libraries in Python like Matplotlib, Seaborn & ggplot for data visualization and also have a hands-on session on a real-life case study.

Topics Covered:

  • Python Basics
  • Data Structures in Python
  • Control & Loop Statements in Python
  • Functions & Classes in Python
  • Working with Data
  • Analyze Data using Pandas
  • Visualize Data 
  • Case Study


  • Know how to install Python distribution like Anaconda and other libraries.
  • Write python code for defining your own functions,and also learn to write object oriented way of writing classes and objects. 
  • Write python code to import dataset into python notebook.
  • Write Python code to implement Data Manipulation, Preparation & Exploratory Data Analysis in a dataset.

Learning Objectives: 

Visit basics like mean (expected value), median and mode. Understand distribution of data in terms of variance, standard deviation and interquartile range and the basic summaries about data and measures. Learn about simple graphics analysis, the basics of probability with daily life examples along with marginal probability and its importance with respective to data science. Also learn Baye's theorem and conditional probability and the alternate and null hypothesis, Type1 error, Type2 error, power of the test, p-value.

Topics Covered:

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


Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario

Learning Objectives: 

In this module you will learn analysis of Variance and its practical use, Linear Regression with Ordinary Least Square Estimate to predict a continuous variable along with model building, evaluating model parameters, and measuring performance metrics on Test and Validation set. Further it covers enhancing model performance by means of various steps like feature engineering & regularization.

You will be introduced to a real Life Case Study with Linear Regression. You will learn the Dimensionality Reduction Technique with Principal Component Analysis and Factor Analysis. It also covers techniques to find the optimum number of components/factors using screen plot, one-eigenvalue criterion and a real-Life case study with PCA & FA.

Topics Covered:

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


  • With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

Learn Binomial Logistic Regression for Binomial Classification Problems. Covers evaluation of model parameters, model performance using various metrics like sensitivity, specificity, precision, recall, ROC Cuve, AUC, KS-Statistics, Kappa Value. Understand Binomial Logistic Regression with a real life case Study.

Learn about KNN Algorithm for Classification Problem and techniques that are used to find the optimum value for K. Understand KNN through a real life case study. Understand Decision Trees - for both regression & classification problem. Understand Entropy, Information Gain, Standard Deviation reduction, Gini Index, and CHAID. Use a real Life Case Study to understand Decision Tree.

Topics Covered:

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


  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.

  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

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

Topics Covered:

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


  • Write python code to Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
  • Write 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.
  • Write Python code to Use Auto Regressive Integrated Moving Average Model for building Time Series Model
  • Dataset including features such as symbol, date, close, adj_close, volume of a stock. This data will exhibit characteristics of a time series data. We will use ARIMA to predict the stock prices.

Learning Objectives:

A mentor guided, real-life group project. You will go about it the same way you would execute a data science project in any business problem.

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor


 Project to be selected by candidates.


Predict House Price using Linear Regression

With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.

Predict quality of Wine using Decision Tree

Wine comes in various styles. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Note:These were the projects undertaken by students from previous batches. 

Data Science with Python

What is Data Science

Dallas is home to many leading companies, including Finoit, Diceus, Intelegain Technologies, RailsCarma, gotcha!, IBEE Solutions, etc. and all these companies are looking for data scientists to make decisions on product and operating metrics. Because of the exclusivity of skills, the average salary for a Data Scientist is $107,806 per year in Dallas, TX. 

Below are the top skills you need to become a data scientist-

  • Python Coding: Python is a popular programming language used in the field of data science - it takes various formats of data and helps in the processing of this data. 
  • SQL database and coding: SQL is an easy-to-learn query language that works on structured data and retrieves data that will be fed to an analytical tool.
  • R Programming: It is an analytical tool that works on the retrieved raw data to arrive at actionable insights. R is an integrated suite of software facilities for data manipulation, mining and calculations. It is an important skill as over 43% of data scientists employ R language for their analysis.
  • Hadoop Platform: Hadoop is a software framework used for big data computation. It processes big data stored in distributed systems.
  • Apache Spark: Apache Spark is also a big data computation platform, not unlike Hadoop. The difference is that Apache Spark is faster, because Spark makes caches of its computations in the system memory, while Hadoop reads and writes to the disk. And owing to this difference, data science algorithms run faster on Apache.
  • Machine Learning and Artificial Intelligence: These are the brains powering recommendation systems that build prediction algorithms using the data and identifying patterns to explore. Some of the concepts used in ML/AI are
      • Reinforcement Learning
      • Neural Network
    • Adversarial learning 
    • Decision trees
    • Machine Learning algorithms
    • Logistic regression 
  • Data Visualization: 

Visualization tools such as d3.js, Tableau, ggplot and matplotlib aid a data scientist in the conversion of complex results obtained as a result of the processes performed on a data set and converts them into a comprehensible visual format.

  • Working with unstructured data:

With diverse datasets coming in, working with unstructured data aids to the 360-degree approach expected of a data scientist. Unstructured data is content that is not labelled and organized into database values such as videos, social media posts, audio samples, customer reviews, blog posts etc.

A successful Data Science professional has the following behavioral traits:

  • Curiosity – As a data scientist, you will be playing with a huge amount of data. To make sense out of it and derive insights, you must have curiosity.
  • Clarity – As a data scientist, you need to have clarity at all times, whether you are cleaning data or writing code. You must be aware of what you are doing and why you are doing at every step.
  • Creativity – A data scientist must be creative to know what is missing from the data and what should be done to get the desired results. This involves developing new features, tools for analysis and ways for data visualization.
  • Skepticism – Skepticism is also an important skill for a data scientist to possess so that they stay in the real world and not get carried away with their creativity.

There are many benefits of ‘Sexiest job of the 21st century’ by Harvard Business review:

  • High Pay- The average salary for a Data Scientist is $107,806 per year in Dallas, TX.
  • Good bonuses- Apart from good pay, you also get good bonuses based on skills and experience. 
  • Mobility- As most of the leading companies are based in the developed countries, you will get a chance to move to these places. 

Data Scientist Skills & Qualifications

To become a successful data scientist, you need to have the following business skills:

  1. Analytic Problem-Solving – You must have a complete understanding of the problem before you start looking for a solution. It helps in the development of strategies required for solving the problem.
  2. Communication Skills – A data scientist must have the skills required to help communicate deep business and customer analytics.
  3. Intellectual Curiosity – If you don’t ask questions like ‘why’ and ‘how’, you are not meant to be a data scientist. You need to have an undying thirst for producing results to provide value to your organization.
  4. Industry Knowledge – To know what is important and what is not, a data scientist must have a strong knowledge of the industry.

Below are the best ways to brush up your data science skills for data scientist jobs:

  • Boot camps

Boot camps are the perfect way to brush up your Python basics. They usually last 4 to 5 days. These boot camps offer theoretical knowledge and hands-on experience.

  • Certifications

Certifications provide you an additional skill set and help improve your CV. Some of the famous data science certifications are:

  • Applied AI with Deep Learning
  • IBM Watson IoT Data Science Certificate
  • Cloudera Certified Associate - Data Analyst
  • Cloudera Certified Professional -  CCP Data Engineer
  • MOOC courses: 

These are online courses that include the latest trends in the industry. These are taught by data science experts and help polish implementation skills in the form of assignments.

  • Projects:

Projects help you to explore new solutions to already answered questions depending upon the project constraints. The more you work on projects, your thinking and skills will get more refined.

  • Competitions:

Competitions like Kaggle, etc. helps you to improve your problem-solving skills by giving restraints and forcing you to find an optimum solution.

We live in a world of data. Most companies in Dallas, Texas including Finoit, Diceus, Intelegain Technologies, RailsCarma, gotcha!, IBEE Solutions, etc., collect data for their own benefit and these data tends to improve their customer experience. You may also get to work with mid-size and small size companies. Mid-size companies have data but they need someone to apply ML techniques to leverage it. Small companies use Google Analytics for their analysis because they have fewer resources and fewer data to work. 

Some ways to practice your data science skills are given below: 

Beginner Level:

  • Iris Data Set:

The Iris data set is said to be the easiest data set. This is the best data set for a beginner and consists of merely 4 columns and 50 rows.

Practice Problem: Predict the class of a flower on the basis of their parameters.

  • Loan Prediction Data Set:

The Loan Prediction data set provides the learner the concepts that are applicable in the domain of banking and insurance - the challenges faced, the variables that influence the outcomes, etc. It consists of 13 columns and 615 rows and is a classification problem data set.

Practice Problem: Predict if a given loan will be approved by a bank based in Dallas or not.

  • Bigmart Sales Data Set:

Operations such as Product Bundling, offer customizations, inventory management, etc. are efficiently handled with the help of Data Science and Business Analytics. The Big Mart Sales Data Set is used in Regression problems and consists of 12 variables and 8523 rows.

Practice Problem: Predict the sales of a retail store of Dallas, Texas.

Intermediate Level:

  • Black Friday Data Set:

The Black Friday Data Set has sales transactions from a retail store. It is an apt data set to expand and explore engineering skills .It has 12 columns and 550,069 rows and is a regression problem.

Practice Problem: Predict the amount of total purchase made on a day in Dallas, Texas.

  • Human Activity Recognition Data Set:

The Human Activity Data Set has a collection of 30 human subjects that were collected via recordings by smartphones. It consists of 561 columns and 10,299 rows.

Practice Problem: Predict the human activity category.

  • Text Mining Data Set: 

This data set consists of aviation safety reports that describe the problems that were encountered on a certain flight. The Text Mining Data Set consists of 30,438 and 21,519 columns. It is a high dimensional and multi-classification problem.

Practice Problem: Classify the documents on the basis of their labels.

Advanced Level:

  •  Urban Sound Classification:

The Urban Sound Classification data set is for implementation of Machine Learning concepts to real-world problems by audio-processing. It consists of 8,732 sound clippings of urban sounds that can be categorized in 10 classes.

Practice Problem: Classify the type of sound that is obtained from particular audio.

  • Identify the digits data set:

This data set comprises of 7000 images, totaling 31MB, with dimensions of 28X28 each. It allows the developer to study, analyze and recognize the elements present in an image.

Practice Problem: Identify the digits present in a given image.

  • Vox Celebrity Data Set:

The Vox Celebrity Data Set is for large scale speaker identification and speech recognition. It is a collection of words spoken by celebrities and extracted from YouTube videos. This data set consists of 100,000 words spoken by 1,251 celebrities around the world.

Practice Problem: Identify the celebrity that a given voice belongs to.

How to become a Data Scientist in Dallas, Texas

Below are the steps to become a successful Data Scientist:

  1. Getting started: Select a programming language that you will be using for data science projects. The most commonly used programming languages in data science are python and R.
  2. Mathematics and statistics: Deciphering patterns in the data and figuring out relationships among them requires the use of mathematics and statistics skills.
  3. Data visualization: You need to learn data visualization for making the data simple and understandable for the non-technical members of the organization. Also, it will help you better grasp the concepts and communicate well with the end users.
  4. ML and Deep learning: To become a successful data scientist, you must be an expert in the fields of deep learning and machine learning. These will be needed for creating the tools that perform the analysis of the data.

Underneath, we have compiled some of the key skills & steps required to get started.

  • Degree/certificate- Texas has many options when it comes to pursuing data science courses, such as Tarleton State University, A & M University, Texas Woman’s University, The University Of Texas at Dallas, etc. You can also opt for online courses offered by KnowledgeHut, Udemy, Udacity, etc. 
  • Unstructured data: This step has the highest complexity. Here, your job is to understand and manipulate unstructured data.
  • Software and Frameworks: It is essential for a data scientist to be comfortable with software, frameworks and programming languages like R.
    • R is the most used programming language to solve statistical problems. At least 43% of data scientists employ this for analysis.
    • Hadoop is the framework used by a majority of data scientists when the amount of data is in excess. Hadoop conveys the data to various points on the machine.
    • Spark is becoming the most popular framework after Hadoop. Spark is used for computational work but is faster than its counterpart. It helps in preventing the loss of data in the data science.
    • It is expected from a data scientist that he/she is proficient in SQL queries.
  • Machine learning and Deep Learning:  Machine Learning is the application of Artificial Intelligence to make working and processing data easier and hassle-free. It is a prerequisite which all organizations expect their prospective Data Scientists to fulfil before joining their team..
  • Data visualization: Data scientists make informed business decisions with data analysis and data visualization. A data scientist’s job is to make sense of the data and make business related charts and graphs. Some of the tools used for this purpose include matplotlib, ggplot2, etc.

Almost 88% of data scientists in Dallas, Texas hold a Master’s degree while 46% are Ph.D. degree holders. Getting a degree is not mandatory but it may help you in networking, internship and recognized academic qualifications in your résumé.

If you are struggling in deciding whether a Master’s degree in data science is right for you or not, here is a scorecard that will help you decide the same. If your total score is more than 6 points, you must get a Master’s degree:

  • Strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • Weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • Non-STEM background: 5 points
  • < 1 year of experience in Python: 3 points
  • 0 year of experience in regular coding for a job: 3 points
  • Not good at independent learning: 4 points
  • Don’t understand that this scorecard is a regression algorithm: 1 point

Knowledge of programming is the fundamental skill for a data scientist. 

Underneath, some reasons are listed:

  • Statistics:The ability to program multiplies a data scientist’s ability to work with statistics.
  • Data sets: Knowledge of programming aids a data scientist in the analysis of large data sets.
  • Framework: Programming enables a data scientist to build frameworks to automatically analyze experiments, visualize data and manage the data pipeline at a large organization so that the data can be accessed by the right person at the right time.

Data Scientist Jobs in Dallas, Texas

Here is a logical sequence of steps which you should follow to get a job as a Data Scientist in Dallas, Texas:

  • Choose a programming language in which you are comfortable. We suggest Python or R language.
  • Mathematics: Data Science is incomplete without Mathematics and Statistics. The data may be numerical, textual or an image. Some of the topics are mentioned below which are important in this field 
    • Descriptive statistics
    • Probability
    • Linear algebra
    • Inferential statistics
  • Libraries: Data science process involves various tasks ranging from preprocessing the data given to plotting the structured data and finally to applying ML algorithms as well. Some of the famous libraries are:
    • Scikit-learn
    • SciPy
    • NumPy
    • Pandas
    • ggplot2
    • Matplotlib
  • Data visualization: It’s your job to make sense of the data given to you by finding patterns and making it as simple as possible. The most popular way to visualize data is by creating a graph. There are various libraries that can be used for this task:
    • Matplotlib - Python
    • Ggplot2 - R
  • Data preprocessing: Due to the unstructured form of data, it becomes necessary for data scientists to preprocess this data to make it analysis-ready.
  • ML and Deep learning: For data analysis, deep learning is highly preferred as deep learning algorithms are designed to work when you have to deal with a huge set of data. It is recommended you spend a few weeks on topics like neural networks, CNN, and RNN as well.
  • Natural Language processing: Every data scientist should be an expert in NLP as it involves the processing of text form of data and its classification as well.
  • Polishing skills: Competitions like Kaggle etc. provide some of the best platforms to exhibit your data science skills. You can try creating your own projects as well.

 Here are the 5 steps you must take if you are preparing to get a job as a data scientist:

  • Study: Brush up on important topics including:
    • Machine Learning
    • Probability
    • Statistics
    • Statistical models
    • Understanding neural networks
  • Meetups and conferences: Start building your network by visiting data science conferences, tech talks, and meetups. This will help you with referrals.
  • Competitions: You can try participating in online and offline competitions that will help you in brushing up on your data science skills. Kaggle is one such online competition.
  • Referral: Referrals have become the primary source of interviews in the IT sector. You need to maintain and update your LinkedIn profile from time to time.
  • Interview: Once you have confidence, start giving interviews. If they turn out how you expected them to be, don’t worry. Learn from your mistakes and study better for the next one.

Some of the major roles & responsibilities of a Data Scientist are:

  • 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 to make sense of the data.
  • Perform statistical analysis for relevant data and predict future outcomes from it.

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 & Education sector: Lowest pay
  •   Roles and responsibilities
    • Data scientist: $107,806/year
    • Data analyst: $73,773/year

A career path in the field of Data Science can be explained in the following ways:

  • Data Mining Engineer: A Data Mining Engineer examines the data for the needs of the business. He also needs to create sophisticated algorithms and create algorithms.
  • Business Intelligence Analyst: A Business Intelligence Analyst has the job of figuring out the business and the market trends. This analysis of data is used to develop a clear picture of where exactly the business stands in the business environment.
  • Data Architect: The role of Data Architect is to work in tandem with system designers, developers and users creating blueprints, used by data management systems to integrate, protect, maintain and centralized data sources.
  • Senior Data Scientist: A Senior Data Scientist is tasked with the anticipation of Business needs in the future and shaping the projects, systems and data analyses of today to suit those business needs in the future.
  • Data Scientist: The main responsibility of a Data Scientist is to pursue a business case by analysis, development of hypotheses and the development of an understanding of data.

Below are the top professional organizations for data scientists –

  • Collin County Data Science Education
  • DFW Data Science
  • Data Science Applications Community (DSAC) - Dallas
  • DFW Data Engineering Meetup
  • North Texas Tableau/Data Viz User Group
  • State Farm Data Science Meetup

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

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

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

Mastery over the following tools or software will help you get preferred over other data scientists:

  • Education: A degree from a prestigious institution will help you jumpstart your career in the field of data science. This can be either an online or an offline course. You can also get some data science certifications to stand apart from the others.
  • Programming: Programming language is a very important skill required to become a data scientist. You need to start with the basics of a programming language and then move on to data science libraries.
  • Machine Learning: Expertise in Machine learning and deep learning skills are must to become a top-notch data scientist. It will help you create tools required to analyze the data.
  • Projects: The more real-world projects you have in your CV, the better your portfolio will be.

Data Science with Python Dallas, Texas

Python is a programming language which is multi-paradigm. Python works on a very simple interface and has high readability. The language also has a wide range of resources which makes it a choice language among the Data Scientists.

Knowledge of programming languages is a must for a job in the field of Data Science. Here are the 5 most popular programming languages commonly used for Data Science:

  • R: R has a steep learninag curve which makes it difficult to learn. However, because of the following advantages, it is one of the most used languages in the data science field.
    • It helps in smooth processing of complex matrix operations with the help of statistical functions.
    • With the help of tool ggplot2, R provides data visualization features.
    • There are several open-source, high-quality packages provided by its open-source community.
  • Python: It is the most preferred language in the data science field. It has fewer packages than R, but it offers the following advantages:
    • It has a very easy to learn, read and understand syntax.
    • It also has the support of a big, open-source community.
    • Tensorflow, pandas, and scikit-learn are python libraries that are used in the field of data science.
  • SQL: You need to have an in-depth knowledge of SQL for working with relational databases. Its syntax is easy to learn, read, understand, and implement. It is very efficient in updating, querying, and manipulating databases.
  • Java: Java has limited verbosity and does not offer many libraries. However, it is still used in Data Science because of the following advantages:
    • Java is a very compatible language. We already have systems in place with Java in the backend code. This makes integrating data science project easier.
    • It is a general purpose, compiled, high-performance language.
  • Scala: Even though it has a complex syntax, Scala is used in several data science projects. Here is why:
    • Since it runs on JVM, Scala is compatible with Java
    • It provides high-performance cluster computing when used with Apache Spark.

To download and install Python 3 on Windows, you should follow these steps:

  •   Download the setup: Go to the download page and set up your python on your windows via the GUI installer. While installing, don't forget to select the checkbox at the bottom which asks you to add Python 3.x to PATH, which is your classpath and will allow you to use python’s functionalities from the terminal.
  •   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

It is very easy to download and install Python 3 on Mac OS X. You can use the .dmg package as well as Homebrew as it makes it easier to install the dependencies as well. You may follow these steps to install Python 3 on Mac OS X.

  • Install xcode: To install brew, you need Apple’s Xcode package, so you should start with the following command:

$ xcode-select --install

  • Install brew: To install Homebrew, you may use the following command:


To confirm if it is installed, you may type brew doctor.

  • Install python 3: To install the latest version of python, you may use:

brew install python

To confirm its version, you should use: python --version

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

reviews on our popular courses

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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!

Ong Chu Feng

Data Analyst
Attended Data Science with Python Certification workshop in January 2020
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Everything from the course structure to the trainer and training venue was excellent. The curriculum was extensive and gave me a full understanding of the topic. This training has been a very good investment for me.

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Cloud Software and Network Engineer
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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Knowledgehut is known for the best training. I came to know about Knowledgehut through one of my friends. I liked the way they have framed the entire course. During the course, I worked on many projects and learned many things which will help me to enhance my career. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

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Junior Software Engineer
Attended Agile and Scrum workshop in May 2018
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I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked his practical way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

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Web Developer.
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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KnowldgeHut's training session included everything that had been promised. The trainer was very knowledgeable and the practical sessions covered every topic. World class training from a world class institue.

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Cloud Consultant
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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It is always great to talk about Knowledgehut. I liked the way they supported me until I got certified. I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked the way of teaching. My special thanks to the trainer for his dedication and patience.

Ellsworth Bock

Senior System Architect
Attended Certified ScrumMaster (CSM)® workshop in May 2018
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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.

Tilly Grigoletto

Solutions Architect.
Attended Agile and Scrum workshop in May 2018
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The course materials were designed very well with all the instructions. The training session gave me a lot of exposure to industry relevant topics and helped me grow in my career.

Kayne Stewart slavsky

Project Manager
Attended PMP® Certification workshop in May 2018


The Course

Python is a rapidly growing high-level programming language which enables clear programs on small and large scales. Its advantage over other programming languages such as R is in its smooth learning curve, easy readability and easy to understand syntax. With the right training Python can be mastered quick enough and in this age where there is a need to extract relevant information from tons of Big Data, learning to use Python for data extraction is a great career choice.

 Our course will introduce you to all the fundamentals of Python and on course completion you will know how to use it competently for data research and analysis. puts the median salary for a data scientist with Python skills at close to $100,000; a figure that is sure to grow in leaps and bounds in the next few years as demand for Python experts continues to rise.

  • Get advanced knowledge of data science and how to use them in real life business
  • Understand the statistics and probability of Data science
  • Get an understanding of data collection, data mining and machine learning
  • Learn tools like Python

By the end of this course, you would have gained knowledge on the use of data science techniques and the Python language to build applications on data statistics. This will help you land jobs as a data analyst.

Tools and Technologies used for this course are

  • Python
  • MS Excel

There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.

On successful completion of the course you will receive a course completion certificate issued by KnowledgeHut.

Your instructors are Python and data science experts who have years of industry experience. 

Finance Related

Any registration canceled within 48 hours of the initial registration will be refunded in FULL (please note that all cancellations will incur a 5% deduction in the refunded amount due to transactional costs applicable while refunding) Refunds will be processed within 30 days of receipt of a written request for refund. Kindly go through our Refund Policy for more details.

KnowledgeHut offers a 100% money back guarantee if the candidate withdraws from the course right after the first session. To learn more about the 100% refund policy, visit our Refund Policy.

The Remote Experience

In an online classroom, students can log in at the scheduled time to a live learning environment which is led by an instructor. You can interact, communicate, view and discuss presentations, and engage with learning resources while working in groups, all in an online setting. Our instructors use an extensive set of collaboration tools and techniques which improves your online training experience.

Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor

Have More Questions?

Data Science with Python Certification Course in Dallas, TX

Known as a city of success where optimism comes face to face with opportunity, Dallas is a highly modern and classy metropolis that attracts worldwide travelers, making it a favored leisure destination in Texas. Once here, visitors can travel by DART, one of the most popular light rail systems in the nation or the momentous free McKinney Avenue Trolley from the Dallas Arts District throughout the upscale area with its pubs, restaurants, hotels, boutique hotels and shops. The strategically situated city is the ninth largest city in the US and just a few hours flight from most North American destinations. Its lively spirit keeps the city alive, and the charitable contributions from its many residents continue to enhance the community and quality of life. Dallas is also the foremost business city. In fact, in 2012, a large number of businesses featured in the Fortune 500 companies, including Southwest Airlines, Exxon Mobil, and Texas Instruments are headquartered here. KnowledgeHut helps you keep pace with the city?s immense prospects by offering you courses like PRINCE2, PMP, PMI-ACP, CSM, CEH and others. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.