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

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

  • Learn Python, analyze and visualize data with Pandas, Matplotlib and Scikit
  • Create robust predictive models with advanced statistics
  • Leverage hypothesis testing and inferential statistics for sound decision-making
  • 220,000 + Professionals Trained
  • 250 + Workshops every month
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Grow your Data Science Skills with Python

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

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  • 42 Hours of Live Instructor-Led Sessions

  • 60 Hours of Assignments and MCQs

  • 36 Hours of Hands-On Practice

  • 6 Real-World Live Projects

  • Fundamentals to an Advanced Level

  • Code Reviews by Professionals

Data Scientists are in high demand across industries


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

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The KnowledgeHut Edge

Learn by Doing

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

Real-World Focus

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

Industry Experts

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

Curriculum Designed by the Best

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

Continual Learning Support

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

Exclusive Post-Training Sessions

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


Prerequisites for the Data Science with Python training program

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

Who should attend the Data Science with Python course?

Professionals in the field of data science

Professionals looking for a robust, structured Python learning program

Professionals working with large datasets

Software or data engineers interested in quantitative analysis

Data analysts, economists, researchers

Data Science with Python Course Schedules

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What you will learn in the Data Science with Python course

Python Distribution

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

User-defined functions in Python

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

Datasets and manipulation

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

Probability and Statistics

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

Advanced Statistics

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

Predictive Modelling

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

Time Series Forecasting

Time Series data, its components and tools.

Skill you will gain with the Data Science with Python course

Python programming skills

Manipulating and analysing data using Pandas library

Data visualization with Matplotlib, Seaborn, ggplot

Data distribution: variance, standard deviation, more

Calculating conditional probability via hypothesis testing

Analysis of Variance (ANOVA)

Building linear regression models

Using Dimensionality Reduction Technique

Building Binomial Logistic Regression models

Building KNN algorithm models to find the optimum value of K

Building Decision Tree models for regression and classification

Visualizing Time Series data and components

Exponential smoothing

Evaluating model parameters

Measuring performance metrics

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Data Science with Python Certification

What is Data Science

Data Science has become a popular career choice in Arlington, Texas. Arlington is known as the center of a web that connects hundreds of federal labs, universities, and corporations in the States. It is also home to many leading companies, such as Life Corp, DolEx Dollar Express, D R Horton, Double B Foods, The Pinnacle, etc. Not just in Arlington, data science has become a boon for many companies around the world. Data Science was also named as the Sexiest job of the 21st century by the Harvard review in 2012. Major companies collect data from users, sell them to the ad companies, and make major profits. How else do you think Amazon knows what to recommend to you when you didn’t even ask for it? The answer is simple, data. Here are some of the reasons that make Data Science the sexiest job of the century:

  1. More and more companies are shifting to data-driven decision making.
  2. We still don’t have enough qualified and experienced data science professionals. So, professionals who are skilled in this domain have the opportunity to get the highest salary in the IT industry.
  3. Today, we are producing more data by the second. When data is collected at such a high rate, it requires great effort in analyzing it. It is the job of a Data Scientist to use this raw data and help the organization make crucial marketing decisions based on it.

Living in Arlington has many advantages as it is home to many universities renowned for data science degrees, such as Southern Methodist University, Tarleton State University, Texas A & M University-College Station, Texas Tech University, etc. You can also opt for online courses and learn at your own pace. If you want to become a Data Scientist, you need to be skilled in the following:

  1. Python Coding: In the field of Data Science, Python is one of the most commonly used programming languages. The simplicity and versatility that Python offers make it the best language for processing of data. It can also take various formats of data. With the help of Python, Data Scientists can create and perform operations on a dataset.
  2. R Programming: If you want to become an expert Data Scientist, you need to have a thorough knowledge and understanding of an analytical tool. R programming makes the problem easy for the data scientists to solve.
  3. Hadoop Platform: Though it is not a requirement, the Hadoop platform is used in several data science projects. So, it is better if you get acquainted with the platform. After doing a study on 3940 jobs on LinkedIn, it was concluded that Hadoop is a leading skill requirement for a Data Scientist.
  4. SQL database and coding: Structured Query Language or SQL is a database language that can be helped in accessing, communicating, and working on the database. MySQL is another such language that uses concise commands and makes the operating process on a database easier by decreasing the technical skills requirement, and therefore saves time.
  5. Machine Learning and Artificial Intelligence: If you want to pursue a career in Data Science, proficiency in Artificial Intelligence and Machine Learning is a must. This requires being familiar with the following concepts:
    • Neural Networks
    • Decision trees
    • Reinforcement learning
    • Logistic regression
    • Adversarial learning
    • Machine learning algorithms, etc.
  6. Apache Spark: Currently, Apache Spark is one of the most popular technologies in the world for data sharing. Like Hadoop, it is used for big data computation. The only major difference between the two is that Apache Spark is faster. This is because Spark uses the system memory to make a cache of its computation while Hadoop reads and writes to the disk. Apache Spark is used to run the data science algorithms faster. It helps prevent the loss of data and can handle complex unstructured datasets. While dealing with large datasets, Spark helps disseminate data processing. The speed with which it operates adds to its advantages. It helps the data scientist carry out the project with ease.
  7. Data Visualization: A data scientist may be able to make sense of the raw data but not every other person shares that skill. It is the job of a data scientist to visualize the data in a format that could be understood by non-tech members of the organization. There are a number of visualization tools for that like Tableau, d3.js, matplotlib, and ggplot. After a number of processes are performed on a dataset, these tools help the data scientists convert the complex results obtained into a format that can be easily understood and comprehended. These tools even help the data scientist quickly grasp insights and provide the right outcome. The organization also gets the opportunity to work directly with the data.
  8. Unstructured data: When it comes to data, most of it is in complex, unstructured form. It is neither labeled nor organized into database values. A data scientist must have the skill to work with this unstructured data. Some of the examples of this unstructured data include social media posts, videos, blog posts, audio samples, customer reviews, etc.

Being a successful data scientist involves incorporating the following behavioral traits:

  • Curiosity – The field of data science involves dealing with a massive amount of data every day. A data scientist must be curious and have an undying hunger for knowledge. Otherwise, it can get too hard too soon. 
  • Clarity – If you are constantly asking questions like ‘how’, ‘why’, and ‘so what’, Data Science might be the perfect field for you. Since the amount of data is so large, getting clarity is very important. During data cleaning or writing code, you must know exactly what you are doing and why you are doing it.
  • Creativity – Creativity is a must in a Data Scientist. It is their job to develop new tools, create new modeling features, and find new ways for data visualization. You need to have the skills to know what is missing and what must be included to get the right insights and outcome.
  • Skepticism – This is what draws the line between a data scientist and a creative mind. It is important for a Data Scientist to be skeptical and keep their creativity in check. This helps them to not get carried away with creativity and stay in the real world.

Arlington is home to many leading companies, such as Life Corp, DolEx Dollar Express, D R Horton, Double B Foods, The Pinnacle, etc. Also being the sexiest job of the 21st century, data scientists enjoy certain benefits over other professions. Here are the 5 proven benefits of being a Data Scientist:

  1. High Pay: When it comes to looking for a job, high pay is expected. Data Science jobs are currently enjoying the boost as compared to other career options. With Data science, the expected remuneration is extremely high. The average salary for a Data Scientist is $68,020 in Arlington.
  2. Good bonuses: When you join a company as a Data Scientist, you will enjoy several perks like signing bonus, equity shares, and impressive bonuses.
  3. Education: Being a data scientist involves getting a Masters or a Ph.D. In this field, knowledge is in great demand. Also, with a degree, you can work as a researcher or lecturer in a government or a private institution.
  4. Mobility: A data scientist job can get you a job in one of the developed countries. This comes with a hefty salary and help improves your living standard.
  5. Network: A data scientist gets to network with other professionals in the tech world through conferences, tech talks, and other platforms. You can use this opportunity for referral purposes. You can also get a research paper published in an international journal.

Data Scientist Skills & Qualifications

It is important to have the following business skills if you want to become a successful data scientist:

  1. Analytic Problem-Solving: The first step of finding a solution to a problem is to understand and analyze it. Before you can find the right strategy to solve it, you need to have a clear perspective of the problem.
  2. Communication Skills: One of the key responsibilities of a data scientist is to communicate deep business and customer analytics to the organization.
  3. Intellectual Curiosity: If you don't have the curiosity to get answers to questions like ‘how' and ‘why', this field is not for you. In order to produce value to the organization, results need to be delivered. And this can be done with a combination of thirst and curiosity.
  4. Industry Knowledge: This is one of the most important business skills a data scientist can have. If you don't have strong industry knowledge, you won't be able to work well with the dataset. You need to have a clear understanding of what needs to be attended and what needs to be ignored.

If you are looking for a job as a Data Scientist in Arlington, here are the 5 best ways to brush up your data science skills:

  • Boot camps: The number of Data Science bootcamps being offered is continuously increasing in Arlington. Attending bootcamps is the perfect way to brush up your programming skills, especially in Python. Lasting for about 4-5 days, these boot camps provide theoretical knowledge and hands-on experience.
  • MOOC courses: MOOCs are the online courses that help you get acquainted with the latest industry trends. Taught by data science experts, these courses come with assignments that help you polish your implementation skills.
  • Certifications: If you want to add an additional skill to your CV and improve it, you should try getting some certifications. Here are some of the data science certifications that you can go for:
  • Cloudera Certified Associate - Data Analyst
  • Cloudera Certified Professional: CCP Data Engineer
  • Applied AI with Deep Learning, IBM Watson IoT Data Science Certificate
  • Projects: Projects are very essential for the refinement of your thinking and skills. Depending on the project constraints, it will help you find new solutions to already solved problems. You might be able to come up with a more efficient solution.
  • Competitions: Competitions help your problem-solving skills. During these, you have to find an optimum solution after following all the restraints and satisfying all the requirements. One such competition is Kaggle.

There are various leading companies headquartered in and around Arlington, TX, such as Life Corp, DolEx Dollar Express, D R Horton, Double B Foods, The Pinnacle, etc. Some companies collect data to sell to other companies while some collect it for their own benefit. Overall, this data is for improving the customer experience. Both types of companies have to hire a data scientist to do the job.

The best way to improve your data science skills is to keep practicing and working your way through Data Science problems. Here, we have categorized different problems according to their difficulty level and your expertise level:

  • Beginner Level
    • Iris Data Set: It is one of the easiest, popular, and versatile data sets available. Used in the field of pattern recognition, the Iris dataset will help you incorporate different learning techniques. If you are a beginner in the field of data science, this dataset is the best for you to embark on your journey. This dataset has just 4 rows and 50 columns. Practice Problem: The problem is using these parameters to predict the class of the flowers. 
    • Loan Prediction Data Set: One of the biggest domains that use data science methodologies for data analysis is the banking domain. While working with this dataset, the learner will have to work with concepts applicable in banking and insurance including the variables that affect the outcome, the implemented strategies and the challenges faced. It is a classification problem dataset with 13 columns and 615 rows. Practice Problem: The problem is to predict if the loan will be approved or not. 
    • Bigmart Sales Data Set: Retail is another such industry that uses data analytics for their business optimization. Data Science and Business Analytics can efficiently handle operations like inventory management, customization, and product bundling, etc. This dataset is a regression problem with 12 columns and 8523 rows. Practice Problem: The problem is predicting the sales of the retail store. 
  • Intermediate Level:
    • Black Friday Data Set: This is a dataset collected from a retail store. With this dataset, you will be able to gain an understanding of the daily shopping experience of millions of customers and also explore and expand your engineering skills. It is a regression problem with 12 columns and 550,069 rows. Practice Problem: The problem is predicting the total amount of purchase.
    • Human Activity Recognition Data Set: This dataset is collected using the recordings of smartphones collected using inertial sensors. It is a collection of 30 human subjects. The dataset consists of 561 columns and 10,299 rows.
      Practice Problem: The problem is the prediction of the category of human activity. 
    • Text Mining Data Set: Obtained by the Siam Text Mining competition held in 2007, the text mining data set consists of aviation safety reports describing the problems encountered on certain flights. It is a multi-classification, high dimension problem with 30,438 rows and 21,519 columns. 
      Practice Problem: The problem is the classification of documents based on their labels. 
  • Advanced Level:
    • Urban Sound Classification: When you are a beginner in the field of Data Science, you try simple and basic machine learning problems like Titanic survival prediction, etc. These can help you get started but they don't provide a taste of the real world problems. The Urban Sound classification is the solution to this. It introduces and implements machine learning concepts to real-world problems. Consisting of 10 classes with 8,732 sound clippings of urban sounds, this problem introduces the developer to the audio processing in the real-world scenarios of classification. 
      Practice Problem: The problem is the classification of the sound obtained from specific audio. 
    • Identify the digits data set: Consisting of 7000 images of 31 MB and 28X28 dimensions, this data set helps you in studying, analyzing, and recognizing elements present in a particular image. 
      Practice Problem: The problem is identifying the digits present in an image.
    • Vox Celebrity Data Set: Audio processing is a developing and important field in deep learning. This dataset is used for large scale speaker identification. It uses YouTube videos to extract the words spoken by celebrities. It is a great example of using isolation and identifying speech recognition. It contains 100,000 words spoken by 1,251 celebrities.
      Practice Problem: The problem is the identification of the voice of a celebrity.

How to Become a Data Scientist in Arlington

Here are the steps that you must follow in order to become a top-notch Data Scientist:

  1. Getting started: First, you have to select a programming language that you have a thorough understanding of and are comfortable using. R and Python are the most preferred languages in the field of Data Science. 
  2. Mathematics and statistics: You need to have a basic understanding of statistics and algebra. This is because in data science you have to deal with data that can be textual, numerical or an image. The job of a data scientist to find patterns and relationships between them. 
  3. Data visualization: One of the most important steps in becoming a Data Scientist, data visualization is required to make the content simple and understandable for the non-technical members of the team. Data visualization is required for better communication with the end users. 
  4. ML and Deep learning: For every data scientist, it is a must to have basic Machine Learning skills along with deep learning skills in their CV. With these, you will be able to analyze any data given to you. 

A job as a Data Scientist sounds very exciting. But the question is how do you become one? Here are some of the steps and key skills required to help you kickstart your career as a data scientist:

  1. Degree/certificate: This is the first step to becoming a Data Scientist. It is not important if that is an online or an offline course as long as it covers the fundamentals. You will see a tremendous boost in your career as you will learn the application of cutting-edge tools used in Machine Learning. Data Scientists have more PhDs than any other job in the tech world due to the rapid advancements in the field. They also have to stay updated and continue learning. 
  2. Unstructured data: The main job of a Data Scientist is the analysis of data. This data is usually in an unstructured format that cannot be fitted into the database. With so much data and the work required to structure this data, the job becomes more complex. It is the job of a data scientist to understand this unstructured data and manipulate it to get optimum results. 
  3. Software and Frameworks: Another important step in becoming a data scientist is learning the usage of software and a framework. You also need to learn a programming language to go along with the framework. The most preferred language in Data Science is Python and R. 
    • R is the most common language used in the field of Data Science for solving statistical problems. It has a steep learning curve. But it is very popular as about 43% of data scientists perform their analysis using the R language. 
    • One of the most commonly used frameworks by Data Scientists is Hadoop. Whenever the amount of data is too much to handle when compared to the memory at hand, it comes into play. The framework is used to convey the data to different points on the machine. Apart from Hadoop, Spark is also becoming quite popular among data scientists. Used for computational purposes, Spark prevents the loss of data that can sometimes happen in Hadoop. It is also faster than Hadoop. 
    • Once you have mastered the framework and the programming language, you can move to databases. A data scientist must be proficient in writing SQL queries. 
  4. Machine learning and Deep Learning: Once you have collected and structured the data, you can start analyzing it by applying algorithms. We can train our model using deep learning techniques and analyze the data. 
  5. Data visualization: Once the data has been visualized, it is the job of a data scientist to visualize this data and make informed decisions on the basis of the analysis. A data scientist converts this raw data into graphs and charts. There are several tools that can be used for visualization like ggplot2, matplotlib, etc.

Getting a degree in Data Science is very essential if you want to land a job as a Data Scientist. About 88% of data scientists have a Master's degree while about 46% have a Ph.D. degree. Also, there are many universities in Arlington offering Mater’s degree in Arlington, such as Southern Methodist University, Tarleton State University, Texas A & M University-College Station, Texas Tech University, etc.A degree is very important because of the following – 

  • Networking – While you are in college pursuing your degree, you will get an opportunity to make acquaintances and friends. This networking will benefit you a lot in the long run as this industry works on referrals.
  • Structured learning – When you are pursuing a degree, you will have to keep up with the curriculum and follow a particular schedule. This is more beneficial and effective than studying without any planning.
  • Internships – This is very important as nothing beats the practical hands-on experience you get from an in-office internship.

  • Recognized academic qualifications for your résumé – If you want a head start in the race for the data scientist jobs, a degree from a prestigious institution will do the trick.

There are many universities in Arlington offering Mater’s degree in Arlington, such as Southern Methodist University, Tarleton State University, Texas A & M University-College Station, Texas Tech University, etc. If you are having trouble in deciding whether you should go for a Master’s degree, you can try grading yourself on the basis of the below scorecard. If your score is more than 6 points, you should get a Master’s degree:

  • A strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • A weak STEM background (biochemistry/biology/economics or another similar degree/diploma): 2 points
  • A non-STEM background: 5 points
  • Less than 1 year of experience in Python: 3 points
  • No experience of a job that requires regular coding: 3 points
  • Independent learning is not your cup of tea: 4 points
  • Cannot understand that this scorecard is a regression algorithm: 1 point

When it comes to becoming a data scientist, the programming language is the most fundamental and important skill regardless of whether you live in Arlington or New York. Here are the reasons why a programming language is required to become a data scientist:

  • Data sets: When it comes to data science, the involvement of large datasets is given. To analyze this large dataset, knowledge of a programming language is a must. 
  • Statistics: A data scientist has to work with statistics. You need the ability to program to implement statistics. Without the knowledge of programming language, knowledge of statistics does not do much good.
  • Framework: If, as a data scientist, you want to perform data analysis properly and efficiently, your programming ability will help you a lot. You will be able to build a system according to the needs of the organization. You would be able to create a framework that could not only automatically analyze experiments, but also manage the data visualization process and the data pipeline. This is done to make sure that the data can be accessed by the right person at the right time.

Data Scientist Jobs in Arlington

If you want to get a job in the field of Data Science, you need to follow this path:

  1. Getting started: First things first, you need to select a language you understand and are comfortable working in. The most commonly used programming languages in Data Science are Python and R language. You also need to understand what being a data scientist actually means and what are their roles and responsibilities.
  2. Mathematics: The work of a data scientist involves making sense of the raw data, finding patterns in the data and then representing them. To successfully perform this, one must have a good knowledge of mathematics and statistics. You need to pay special attention to linear algebra, probability, inferential statistics, and descriptive statistics.
  3. Libraries: There are various processes involved in Data Science including preprocessing the data, plotting the structured data and applying machine learning algorithms to the data. For this, several libraries can be used like Pandas, Matplotlib, SciPy, Scikit-learn, NumPy, ggplot2, etc.
  4. Data visualization: As a data scientist, it is your job to find the sense of the raw data provided to you, find relevant patterns and make it simple for the non-technical members of the team. This can be done by visualizing the data using a graph. The libraries used for this task are ggplot2 and matplotlib.
  5. Data preprocessing: As most of the data we have is in an unstructured form, it is very important to preprocess the data so that it is ready for the analysis. It can be done using variable selection and feature engineering. Once the preprocessing is completed, we get the data in a structured form that can be then injected into the Machine Learning tool for the analysis.
  6. ML and Deep learning: You need to have Machine learning and deep learning skills in your CV to get a job as a data scientist. Deep learning algorithms are used while dealing with a huge set of data. You need to have a tight grasp on topics like CNN, RNN, Neural networks, etc.
  7. Natural Language processing: Natural language processing involves processing and classification of textual data. Every data scientist must be an expert in NLP.
  8. Polishing skills: You can exhibit your data science skills in competitions like Kaggle. You can also explore the field by experimenting and creating your own projects.

The 5 important steps to prepare for the job as a Data Scientist involves:

  • Study: While you are preparing for the interview you need to cover all the basic and important topics like statistics, statistical models, probability, neural networks, machine learning, etc.
  • Meetups and conferences: You need to expand your professional connections and start building your own network. You can meet other data science professionals in conferences, tech talks, meet-ups, etc.
  • Competitions: You need to keep practicing, implementing, polishing, and testing your skills through online competitions like Kaggle.
  • Referral: According to a survey, the primary source of interviews in companies is a referral. Keep your LinkedIn profile updated.
  • Interview: Once you think that you are ready for the interview, go for it. It might take a couple of interviews before you land a job. Don't lose hope after a bad interview. Instead, study the questions you weren't able to answer.

The main aim of a data scientist is to search the raw data or patterns and inference information from it to meet the needs and goals of the business. This data can be present in the form of structured as well as unstructured data.

In the modern world, tons of data are generated every day. This has made the job of a data scientist all the more important. This data is a gold mine of ideas and patterns that can give the business a tremendous growth. It is the job of a data scientist to extract the relevant information from this vast amount of data and benefit the business.

Data Scientist Roles & Responsibilities:

  • The first and the most important role of a data scientist is to get the data that is relevant to the business from the huge amount of data provided to them. This data can be in structured as well as unstructured form.
  • Next, comes the organization and analysis of data from the piles of data.
  • Once you have analyzed the data, you need to create machine learning techniques, tools and programs to identify patterns in the data and make sense out of it.
  • Lastly, you need to perform statistical analysis on the data to predict future outcomes.

Being the sexiest job of the 21st century comes with its perks. High demand and less supply of data scientists have spiked their base salaries 36% higher than any other predictive analytics professional. The earning of a data scientist depends on the following things:

  • Roles and responsibilities
    • Data scientist: $105,975/yr
    • Data analyst: $68,020/yr
  • Type of company
    • Public: Medium pay
    • Startups: Highest pay

A Data Scientist has the skills of a computer scientist, a mathematician, and a trend spotter. The main part of a Data Scientist's job is to mine the huge volume of data to decipher patterns and find relationships. This is then used to make predictions for the future. The whole career path of a Data Scientist can be explained as follows:

Business Intelligence Analyst: It is the responsibility of a business intelligence analyst to figure out the business and keep a check on the latest market trends. This can be done by performing the analysis of the data provided by the organization. One needs to have to clear picture of where the organization stands in the business environment.

Data Mining Engineer: The job of a data mining engineer is to examine the data for the business. They often work as a third party. Apart from examining the data, they are also needed for the creation of algorithms that are required in the further data analysis.

Data Architect: Data Architects work alongside developers, system designers, and users. They create the blueprints that are used for the integration, protection, centralization, and maintenance of the data sources. These blueprints are used by data management systems.

Data Scientist: A Data Scientist has the responsibility of doing the analysis, pursuing a business case, developing hypotheses, understanding the data, and exploring patterns from the provided data. After this, comes the development of systems and algorithms that can find a way to use this data in a productive manner. This further improves the interest of the business.

Senior Data Scientist: A Senior Data Scientist is one who anticipates the future needs of the business and shapes the projects according to that. This includes modifying the data analysis process and systems to suit the needs of the future.

Below are the top professional organizations for data scientists in Arlington – 

  • Data Science DC
  • Data Science Dojo – Washington DC
  • Data Education DC
  • Full Stack Data Science

If you want to get hired fast in Arlington, referrals are the way to go. You can create your network with other Data Scientists through the following:

  • Online platforms like LinkedIn
  • Data Science conference
  • Social gatherings like meetups

There are several career options for a data scientist in Arlington. These include – 

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

Arlington is home to the University of Texas, a major urban research university and hence employers in Arlington generally prefer data scientists to have mastery over some software and tools. They generally look for:

  • Education: Getting a degree in Data Science, like a Master's degree or a Ph.D., will benefit you a lot in the long run. You can also try getting some certifications.
  • Programming: Programming is one of the most important skills required to be a data scientist. You can try Python or R programming language. Before you move on to any data science libraries, you must learn Python basics.
  • Machine Learning: Once you have collected the data and converted it into a structured form, you will need deep learning and machine learning skills to find relationships and analyze patterns.
  • Projects: You must try exploring old projects and creating new ones to build your portfolio. You need to try your hands on real-world projects to improve your skills and build your portfolio.

Data Science with Python Arlington

  • Multi-paradigm programming language – Python is a programming language with various facets that makes it most suited for the Data Science field. It is an object-oriented, structured programming language that comes with several packages and libraries that can be beneficial in the field of Data Science.
  • Simple and readable – It is one of the most commonly preferred languages used by Data Scientists because of its simplicity and readability. There are a vast number of dedicated packages and analytical libraries that are customized to be used in the field of Data Science. This makes it more attractive to Data Scientists as compared to any other programming language.
  • Wide range of resources – Python is a programming language that comes along with a diverse range of resources. Whenever a data scientist is developing a program for Data Science model in Python and gets stuck, they have these resources available at their disposal.
  • The Python community – The other benefit of using Python as a programming language in Data science is the vast community dedicated to the language. Currently, there are millions of developers who are working on the same programming language and the same problem every single day. So, as a developer, you will get plenty of help in resolving your problems because there is a huge possibility that someone has gone through the same issue and found its solution. Even if there is no solution available, the Python community will help never step back from helping a fellow Python developer.

Here are the 5 most popular programming languages used in the Data Science field:

  • R: Even though the language has a steep learning curve, it offers the following advantages:
    • There are many high-quality open source packages provided by the big, open source community of the language. 
    • The language is capable of handling complex matrix equation while dealing with loads of statistical functions smoothly.
    • R can be used with ggplot2 to provide data visualization. 
  • Python: It is one of the most commonly used programming languages in the field of data science even though it has fewer packages than R. It is because of the following advantages that it offers:
    • It very easy to learn, understand and implement.
    • It has the support of a big open-source community as well.
    • It has most of the libraries that you might need for data science like scikit-learn, tensorflow, and Pandas.
  • SQL: Required for working with relational databases, SQL is a structured query language that has the following benefits:
    • It has a pretty easy to write and read syntax.
    • It is very efficient in manipulating, updating, and querying relational databases. 
  • Java: Java has fewer libraries and its verbosity is limited, but it has certain advantages:
    • There are several systems that are already coded in Java at the backend. This makes the integration of data science projects to these systems easy. 
    • It is a general purpose, high-performance, and a compiled language. 
  • Scala: It is a preferred language in data science even though it has a complex syntax because of the following reasons:
    • The language runs on JVM that makes it compatible with Java as well.
    • If the language is used with Apache Spark, we can get high-performance cluster computing.

Here is how you can download and install Python 3 on Windows:

Download and setup: Visit the download page and use the GUI installer to setup Python on your windows. Make sure that while you are installing, you select the checkbox asking to add Python 3.x to PATH. This is your classpath that will allow you the usage of Python's functionalities from the terminal.

You can also use Anaconda to install Python. If you want to check if Python is installed, you can try using the following command that will show the current version of Python installed:

python --version

  • Update and install setuptools and pip: If you want to install and update the crucial libraries, you can use the following command:

python -m pip install -U pip

Note: You can create isolated Python environments and pipenv using virtualenv. Pipenv is a Python dependency manager. 

For installing Python 3 on Mac OS X, you can either simply install the language from their official website using a .dg package or use Homebrew python or its dependencies. Here are the steps you need to follow:

  1. Install Xcode: First, you need to install Xcode. You will need the Xcode package of Apple/ Start using the following command: $ xcode-select --install
  2. Install brew: Next, you have to install Homebrew which is a package manager for Apple. Start with the following command:/usr/bin/ruby -e "$(curl -fsSL" Confirm if it is installed by typing: brew doctor
  3. Install python 3: Lastly, to install python, use the following command: brew install python
  • If you want to confirm the version of python, use the command: python --version

You should install virtualenv that will create isolated places for you to run different projects and can even run different versions of Python on different projects.

Data Science with Python Course Curriculum

Download Curriculum

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


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

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

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


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


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

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

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


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


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

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

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


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


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

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

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


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


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

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

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


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


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

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


  • Project to be selected by candidates.

FAQs on the Data Science with Python Course

Data Science with Python Training

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

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

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

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

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

The Data Science with Python course is ideal for:

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

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

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

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

Other important skills are

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

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

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

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

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

Hardware requirements

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

Software Requirements

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

System Requirements

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

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

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

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

Below is the roadmap to becoming a data scientist:

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

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

Data Science with Python Workshop

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

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



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

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

Daiv D Souza Senior Developer

The learning methodology put it all together for me. I ended up attempting projects I’ve never done before and never thought I could. 

Attended Front-End Development Bootcamp workshop in July 2021

Dave Nigels Full Stack Engineer

The learn by doing and work-like approach throughout the bootcamp resonated well. It was indeed a work-like experience. 

Attended Back-End Development Bootcamp workshop in May 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 Back-End Development Bootcamp workshop in April 2021

Elyssa Taber IT Manager.

I would like to thank the KnowledgeHut team for the overall experience. My trainer was fantastic. Trainers at KnowledgeHut are well experienced and really helpful. They completed the syllabus on time, and also helped me with real world examples.

Attended Agile and Scrum workshop in June 2020

Kayne Stewart slavsky Project Manager

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.

Attended PMP® Certification workshop in June 2020

Anabel Bavaro Senior Engineer

The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut. I really liked the way the trainer explained the concepts. He was very patient and well informed.

Attended Certified ScrumMaster (CSM)® workshop in August 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 Arlington, TX

Situated in a state that is rich in history and myth, of legendary cowboys and buried treasures, Arlington is today a vibrant financial centre that houses national and international conglomerates and world class universities. A sporty city to the core, it is home to the Texas Rangers baseball team and several stadiums that host annual sporting events with much fanfare. There are also a number of amusement parks and nature trails to keep one busy over the weekends. This is a great place to start your career and KnowledgeHut helps you along the way by offering internationally recognized courses such as PRINCE2, PMP, PMI-ACP, CSM, CEH, CSPO, Scrum & Agile, MS courses, Big Data Analysis, Apache Hadoop, SAFe Practitioner, Agile User Stories, CASQ, CMMI-DEV 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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