Data Science with Python Training in Dallas, 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
  • 70 + 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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Highlights

  • 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

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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

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 this 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

1

Python Distribution

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

2

User-defined functions in Python

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

3

Datasets and manipulation

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

4

Probability and Statistics

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

5

Advanced Statistics

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

6

Predictive Modelling

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

7

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Data Science with Python Course Curriculum

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


Topics

  • 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

Topics

  • 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

Hands-on

  • 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

Topics

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

Hands-on

  • 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.

Topics

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

Hands-on

  • 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

Topics

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

Hands-on

  • 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.

Topics

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

Hands-on

  • 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.


Hands-on

  • 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 support@knowledgehut.com and we will be happy to get back to you.

What Learners Are Saying

O
Ong Chu Feng Data Analyst
4
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

L
Lauritz Behan Computer Network Architect.
5

Overall, the training session at KnowledgeHut was a great experience. I learnt many things. I especially appreciate the fact that KnowledgeHut offers so many modes of learning and I was able to choose what suited me best. My trainer covered all the topics with live examples. I'm glad that I invested in this training.

Attended PMP® Certification workshop in May 2020

S
Steffen Grigoletto Senior Database Administrator
5

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

Attended PMP® Certification workshop in April 2020

I
Ike Cabilio Web Developer.
5

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.

Attended Certified ScrumMaster (CSM)® workshop in June 2020

H
Hillie Takata Senior Systems Software Enginee
5

The course material was designed very well. It was one of the best workshops I have ever attended in my career. Knowledgehut is a great place to learn new skills. The certificate I received after my course helped me get a great job offer. The training session was really worth investing.

Attended Agile and Scrum workshop in August 2020

A
Alexandr Waldroop Data Architect.
5

The workshop held at KnowledgeHut last week was very interesting. I have never come across such workshops in my career. The course materials were designed very well with all the instructions were precise and comprehenisve. Thanks to KnowledgeHut. Looking forward to more such workshops.

Attended Certified ScrumMaster (CSM)® workshop in January 2020

M
Marta Fitts Network Engineer
5

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

E
Ellsworth Bock Senior System Architect
5

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.

Attended Certified ScrumMaster (CSM)® workshop in February 2020

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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:

/usr/bin/ruby-e"$(curl-fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"

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.

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.

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