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

Get the ability to analyze data with Python using basic to advanced concepts

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

Description

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

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

This Data science for Python programming course is an umbrella course covering major Data Science concepts like exploratory data analysis, statistics fundamentals, hypothesis testing, regression classification modeling techniques and machine learning algorithms.Extensive hands-on labs and an interview prep will help you land lucrative jobs.

What You Will Learn

Prerequisites

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

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

Who should Attend?

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

KnowledgeHut Experience

Instructor-led Live Classroom

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

Curriculum Designed by Experts

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

Learn through Doing

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

Mentored by Industry Leaders

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

Advance from the Basics

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

Code Reviews by Professionals

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

Curriculum

Learning Objectives:

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

Topics Covered:

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

Hands-on:  No hands-on

Learning Objectives:

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

Topics Covered:

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

Hands-on:

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

Learning Objectives: 

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

Topics Covered:

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

Hands-on:

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

Learning Objectives: 

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

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

Topics Covered:

  • 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, you are required to build a regression model to predict the property prices.
  • Reduce Data Dimensionality for a House Attribute Dataset for more insights & better modeling.

Learning Objectives: 

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

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

Topics Covered:

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

Hands-on: 

  • With various customer attributes describing customer characteristics, build a classification model to predict which customer is likely to default a credit card payment next month. This can help the bank be proactive in collecting dues.
  • Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
  • Wine comes in various types. With the ingredient composition known, we can build a model to predict the Wine Quality using Decision Tree (Regression Trees).

Learning Objectives:

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

Topics Covered:

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

Hands-on:  

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

Learning Objectives:

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

Topics Covered:

  • Industry relevant capstone project under experienced industry-expert mentor

Hands-on:

 Project to be selected by candidates.

Meet your instructors

Biswanath

Biswanath Banerjee

Trainer

Provide Corporate training on Big Data and Data Science with Python, Machine Learning and Artificial Intelligence (AI) for International and India based Corporates.
Consultant for Spark projects and Machine Learning projects for several clients

View Profile

Projects

Predict House Price using Linear Regression

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

Predict credit card defaulter using Logistic Regression

This project involves building a classification model.

Read More

Predict chronic kidney disease using KNN

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

Predict quality of Wine using Decision Tree

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

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

Data Science with Python Certification

What is Data Science

Data Science has become a popular career choice worldwide. Austin, Texas is also witnessing an increase in the number of small-scale as well as big corporations that are starting to rely on Data Science for their decision making process. Being home to companies like Amazon, Whole Foods Market, Smarter Sorting, Cerebri AI, Finastra, Oracle, DELL, Macmillan Learning, etc. that uses Data Science for optimization, Austin has become a hub for Data Scientists to look for jobs.

In 2012, it was named as the sexiest job of the 21st century by the Harvard Review. The reason behind this is the data. We have so much data and there are so many ways to benefit from this data. Companies like Google and Facebook collect this data and sell these to ad companies to earn profits. How else do you think Amazon is able to show you products that you didn’t explicitly ask for? Here are the reasons for the popularity of data science:

  1. Decision making based on data is in demand right now.
  2. Data scientists have the highest demand and earning potential in the tech world due to a lack of qualified and experienced data science professionals.
  3. Today, we produce data at an exceptionally high rate. So, more efforts are required to analyze it. Based on the findings from this raw data, a data scientist can help the management take important marketing decisions.

Austin, TX is home to several tech corporations, such as IBM, Dell, Adobe, Apple, Amazon etc. that require the data scientists for optimizing their business. To get this job, you need to work on your technical skills. The University of Texas has a course in Data Science that will help you cover all the basic technical skills required to be a top-notch data scientist. You can also opt for online courses or bootcamps. To become a data scientist in Austin, TX, USA, you need to have the following technical skills:

  1. Python Coding: Python is undoubtedly one of the most popular and preferred programming languages used in the data science field. It can take various data formats and aids in the data preprocessing. It is very simple and versatile that gives it an advantage over other programming languages. It also allows data scientists to create and perform operations on dataset.
  2. R Programming: If you want to make a data science problem easy to solve, you need to have the knowledge of R programming. To become a master data scientist, you need to have a comprehensive knowledge of an analytical tool.
  3. Hadoop Platform: Knowledge of Hadoop platform is not a must for data science, but it is used in several data science projects. So, it will be better if you have knowledge of the Hadoop platform.
  4. SQL database and coding: SQL, or Structured Query Language, is used by the data scientist for accessing, working, and communicating data. This helps the data scientist gain an understanding of the structure and formation of a database. MySQL is another such language with concise commands that saves time and does not require an expert technical skill level for performing operations on the database.
  5. Machine Learning and Artificial Intelligence: To be a successful data scientist, one needs to be proficient in Machine Learning and Artificial Intelligence. As a potential data scientist, you must make yourself efficient with topics like neural networks, decision trees, logistic regression, reinforcement learning, adversarial learning, machine learning algorithms, etc.
  6. Apache Spark: Used as a data sharing technology, Apache Spark is like Hadoop where it is used for big computation. It is faster than Hadoop. This is because Spark caches its computations in the memory of the system whereas Hadoop reads and writes to the disk. So, Apache is used for running the data science algorithms faster. Apache Spark also helps in disseminating data processing. It is well equipped to deal with large datasets and handle complex unstructured data. Unlike Hadoop, it can prevent loss of data. The most important factor of why it is so preferred in the field of Data Science is the speed and ease with which it allows the data scientists to carry out the project.
  7. Data Visualization: Once the data has been analyzed, a data scientist has to be able to present this in a form that is understandable to the non-technical members of the team. There are several visualization tools available for this purpose like Tableau, ggplot, d3.js, and matplotlib. Complex results are converted into a format that is easy to understand and comprehend. These results are obtained after a series of processes performed on a dataset. Data visualization also allows the organization to work with data. Data scientists can easily grasp the insights from the data and act on the new outcome.
  8. Unstructured data: Most of the data that is generated is unlabelled, not organized into databases values, and unstructured. This unstructured data includes blog posts, audio samples, videos, customer reviews, social media posts, etc.

The top 5 essential behavioral traits of a successful Data Science professional include:

  • Curiosity – There is a huge amount of data that is generated every single day. It is in different formats and sometimes it is hard to make any sense out of it, let alone derive insights. So, a curious nature and undying hunger for knowledge are essential; otherwise, it can become too hard too soon.
  • Clarity – If you are the type of person who asks questions like ‘so what’ and ‘why’ and lives for clarity, data science is the field for you. Whether you are writing code or cleaning up data, you need to know at every step what you are doing and why are you doing it.
  • Creativity – With so much data, a data scientist has to sometimes figure out what is missing and what must be added to get results. This requires creativity. It includes developing new modeling features, tools for analysis and finding ways for data visualization.
  • Skepticism – This is where the line is drawn between a creative mind and a data scientist. A data scientist needs skepticism to not get carried away with their creativity and stay in the real world.

Austin, TX is home to corporations looking for Data Scientists. Some of them include Clockwork Solutions, Siemens, CCC Information Services Inc., CDK Global, PerkinElmer, Asuragen, EY, DE Power, Red Ventures, Forcepoint, etc. If you are a data scientist, you will enjoy the following benefits:

  1. High Pay: The data scientists are the highest paid professionals in the IT industry. This is because of the issue of high demand and low supply. The average salary for a Data Scientist is $111,250 per year in Austin, TX.
  2. Good bonuses: When you join a company as a data scientist, you can expect signing perks, equity shares, and impressive bonuses.
  3. Education: You need to have a Master's degree or a Ph.D. to become a data scientist. There is a huge demand for knowledge in this field. So, you can try getting a job as a lecturer or a researcher in a government or private institution.
  4. Mobility: Most of the organizations that hire data scientists are located in developed countries that give you an opportunity to improve your living standard and get a hefty salary.
  5. Network: You will get lots of opportunities of getting involved in the tech world through research papers, meeting people at tech talks, conferences, and meetups. This will help you create a network with other data science professionals which you can use for referral purposes.

Data Scientist Skills & Qualifications

The top 4 must-have business skills required to become a data scientist include:

  1. Analytic Problem-Solving – Before you can find a solution to a problem, you need to understand and analyze the problem. Once that is done, you will have a clear perspective to develop the right strategies needed for solving the problem.
  2. Communication Skills – It is the responsibility of a data scientist to help the business communicate deep business and customer analytics.
  3. Intellectual Curiosity – If you are not curious to get answers to questions like ‘why', you are not meant to be a data scientist. To produce insights into a commercial enterprise, a data scientist must be curious and have an undying thirst for delivering results.
  4. Industry Knowledge – This is one of the most important skills in a data scientist. If a data scientist doesn’t have a strong knowledge of the industry, he/she won’t know what needs attention and what doesn’t.

Before you go for the interview for a job as a data scientist in Austin, TX, you need to brush up your data science skills. Here are the 5 best ways to do it:

  • Boot camps: If you want to have theoretical knowledge and hands-on experience while brushing up your data science skills, boot camps are the perfect way to do that. They usually last about 4 to 5 days and you will be able to brush up your Python basics.
  • MOOC courses: MOOC is an online course taught by data science experts that will give you assignments to help you polish your implementation skills and get the knowledge of the latest trends in the industry.
  • Certifications: The next step is to get some certifications that will help you build your portfolio. 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: Nothing can help you brush up your data science skills as much as working on projects. It will help you find new solutions to already answered questions. You can also work on new projects that can refine your skills.
  • Competitions: You can try using online competitions like Kaggle where you can improve your problem-solving skills. You need to follow the restraints, satisfy all the requirements, and find an optimum solution.

There are several organizations hiring data scientists in Austin, TX; some for their own benefits and some to use as a third party. These organizations include Clockwork Solutions, Siemens, CCC Information Services Inc., CDK Global, PerkinElmer, Asuragen, EY, DE Power, Red Ventures, Forcepoint, Amazon, Whole Foods Market, Smarter Sorting, Cerebri AI, Finastra, Oracle, DELL, Macmillan Learning, etc.

Practicing and working is the best way to master any skills. Similarly, you need to work your way through the data science problem as well. Here are a few ways categorized on the basis of difficulty and expertise level:

  • Beginner Level
    • Iris Data Set: This dataset consists of 4 columns and 50 rows. It is one of the most popular, easy, resourceful, and versatile datasets available for pattern recognition. You will be able to learn various classification techniques while dealing with this dataset. This is a great dataset for beginners to start in the field of data science.Practice Problem: Use the parameters to predict the class of a flower.
    • Loan Prediction Data Set: Consisting of 13 columns and 615 rows, this dataset is from the banking domain which uses more data science methodologies and data analytics than any other industry. With the loan prediction dataset, you will have to work with concepts used in the banking and insurance domain like strategies implemented, variables that can affect the outcome, and challenges faced. It is a classification problem dataset.Practice Problem: Predicting if a certain load will be approved or not.
    • Bigmart Sales Data Set: Retail sector is another industry that relies heavily on analytics for optimizing their business processes. This dataset contains 12 variables and 85223 rows. Data Science and business analytics can help in the handling of operations like inventory management, customizations, product bundling, etc. This dataset is a regression problem.
      Practice Problem: Predicting the sales of the store.
  • Intermediate Level:
    • Black Friday Data Set: This is a large dataset with 12 columns and 550,069 rows. This dataset is a regression problem consisting of sales transactions of customers in a retail store. If you want to understand the daily shopping experience of millions of customers and at the same time explore and expand your engineering skills, this is an apt dataset for you.
      Practice Problem: The problem is predicting the total purchase amount.
    • Human Activity Recognition Data Set: Consisting of 561 columns and 10,299 rows, the human activity data set was collected from 30 human subjects via smartphone’s recordings. These smartphones were embedded with inertial sensors.
      Practice Problem: The problem is predicting the category of human activity.
    • Text Mining Data Set: With 30,438 rows and 21,219 columns, this data set is a multi-classification, high dimensional problem dataset that was collected during the 2007's Siam Text Mining competition. The dataset had safety reports of aviation describing problems that occurred during the flights.
      Practice Problem: The problem is using the labels to classify the documents.
  • Advanced Level:
    • Urban Sound Classification: There are several basic and simple machine learning problems like the Titanic survival prediction that you will study in the beginning. But these problems do not help you get familiar with the real world problems. This is where urban sound classification comes in. It will help you implement machine learning skills to real-world problems. It also includes the use of audio processing for real-world scenarios of classification. It has 8,732 sound clippings belonging to 10 different classes.
      Practice Problem: The problem is identifying particular audio and classifying it to its class.
    • Identify the digits data set: This 31 MB dataset has 7000 images of 82X28 dimensions each. The learner has to study, analyze and recognize the elements present in the image.
      Practice Problem: The problem is identifying various elements present in the image.
    • Vox Celebrity Data Set: This dataset introduces you to the usage of audio processing in deep learning. This dataset is a large scale speaker identification problem consisting of 100,000 words spoken by 1,251 celebrities. This dataset is extracted from YouTube videos. It is a great example to practice isolating and identifying speech.
      Practice Problem: The problem is identifying the voice of the celebrity.

How to Become a Data Scientist in Austin, Texas

Becoming a data scientist is easy if you know the right steps to take and have the right guidance. Here are the right steps to becoming a top-notch Data Scientist:

  1. Getting started: First things first, you need to select a programming language that you can use in Data Science and you know well enough to be comfortable in. The most preferred programming languages used in Data Science are Python and R.
  2. Mathematics and statistics: The field of Data Science is all about data. This data can be in the form of a text, numbers or even an image. It is the job of a data scientist to decipher a pattern in this data and figure out the relationship between them. For this, knowledge of basic algebra and statistics is required.
  3. Data visualization: When it comes to Data Science, Data visualization is one of the most important steps. When you will work as a data scientist, you will be working with a team which will include some non-technical members as well. You need to make your data as simple as possible so that everyone is able to understand it and grasp its contents. You need to learn data visualization for communicating with end users as well.
  4. ML and Deep learning: It goes without saying that to become a successful data scientist; you must be an expert in deep learning as well as machine learning skills. With these skills, you will be to analyze all the data provided to you.

Harvard Business Review declared the job of a Data Scientist as the sexiest job of the 21st century in 2012. Needless to say, data scientists are quite in demand right now and data science is a popular career choice. But, how do you prepare for a career in data science? 

Here are some key steps and skills that will help you become a successful data scientist:

  1. Degree/certificate: Getting a degree in Data Science will help you a lot. It will give you a tremendous boost in career growth. A degree from a prestigious institution will put you ahead of all the other competitors without a degree. You can go for an offline or online classroom program. This will help you cover all the fundamentals and get an understanding of the cutting-edge tools used in the data science field. Data Science is continuously advancing and due to this, you need to keep studying. This is the reason why so many data scientists are Ph.D. holders.
  2. Unstructured data: The main part of the job of a data scientist is to analyze the data to find patterns. But most of this data is in an unstructured form that cannot fit into the database. This part is very complex because you will be spending a lot of time and effort just to structure the data to make it useful and ready for analysis. Only after this, you will be able to understand and manipulate the data.
  3. Software and Frameworks: Thanks to the various software and frameworks, dealing with the unstructured data has become easy. You must be comfortable working in these frameworks with the software and tools to help in the analysis of the data. Apart from this, knowledge of a programming language is essential as well because without it you won't be able to implement anything.
    • R is considered a complex language with a steep learning curve. However, it is one of the most preferred languages by the data scientists. About 43% of data scientists perform analysis using the R programming language. It helps a lot in handling statistical problems.
    • When the amount of data to be processed is significantly higher than the memory at hand, the majority of data scientists use the Hadoop framework. The framework is used for quickly conveying the data to the different parts of the machine. Another popular framework after Hadoop is Spark. When it comes to computational work, Spark is faster than Hadoop. This is because Spark caches the computation in system memory while Hadoop reads and writes it to the disk. Also, unlike Hadoop, Spark prevents data loss in the analysis.
    • Once you have a thorough understanding of the frameworks, software and the programming language, you need to get a complete knowledge of databases as well. A data scientist must be able to easily read and read SQL queries.
  4. Machine learning and Deep Learning: Once the data has been collected and prepared, the data scientist will move on to further analyze the data by applying algorithms. Deep learning can be used to train the model in dealing with the data it is provided.
  5. Data visualization: Once the data has been analyzed, it is the job of a data scientist to visualize the data in a form that is easily understandable by everyone. It is the job of a data scientist to analyze the data, visualize the data and then make informed business decisions. Graphs and charts are used to make sense of the huge amount of data provided to the data scientist. There are several tools available for the job including ggplot2, matplotlib, etc.

A degree in Data Science will help a lot in getting a job. About 88% of all data scientists have a Master's degree while 46% of them are Ph.D. holders. The University of Texas has a course in Data Science that will help you cover all the basic technical skills required to be a top-notch data scientist:

The advantages of getting a degree in Data Science include:

  • Networking – Networking is an essential part of the IT industry. When you are getting a degree, you will be able to make friends and acquaintances, helping you land a better job in the future.
  • Structured learning – Once you have started pursuing a degree, you will have to keep up with the curriculum and follow a particular schedule. This is especially important for people who are not good at independent learning.
  • Internships – During the degree, you will have to get an internship. This is a very important aspect as through the internship, you will be able to get practical hands-on experience.

  • Recognized academic qualifications for your résumé – Once you have a degree from a prestigious institution, it will improve your CV and kickstart your data science career.

Most of the people struggle in deciding if they should get a master's degree in data science or not. The University of Texas, Austin has a course in Data Science that will help you cover all the basic technical skills required to be a top-notch data scientist. Here is a scorecard that will grade you and help you in deciding whether you need a master's degree or not. If your total is greater than 6 points, a master's degree is advised:

  • 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

Programming knowledge is the most fundamental and important skills required to become a data scientist. Here are the reasons why knowledge of programming language is a must:

  • Data sets: While working in data science, you will have to work with a huge amount of datasets. To analyze these large datasets, a programming language will be required.
  • Statistics: Knowledge of statistics is required to become a data scientist. But to work with statistics, programming ability is essential. Statistics knowledge will become much less useful if the data scientist does not have the knowledge of any programming language to implement it. Without programming language, you won’t be able to apply statistics in Data Science.

  • Framework: To analyse and work with data science properly and efficiently, a data scientist must have an in-depth knowledge of a programming language. With a programming language, a data scientist will be able to build a system that can be useful for the organization or create frameworks that will be able to automatically analyze experiments, manage the data pipeline, and visualize the data. This also helps in getting access to data at the right time by the right person.

Data Scientist Salary in Austin, Texas

A Data Scientist based in Austin earns a median salary of $95,261 on a yearly basis

The average annual salary of a Data Scientist in  Atlanta is $88,603, which is $6,658 less than that of Austin. 

Data Scientist in Austin earns $95,261 per year, which is slightly lower than a data scientist working in Los Angeles at an income of $98,294 per year. 

The city of Sacramento in California has an average pay of $121,590 per year for data scientists. 

The annual income of data scientists in other Texas cities like Dallas and Houston is $84,500 and $88,274 respectively. 

Owing to the entry of Data Science in every big and small corporation in Texas, the demand for Data Scientists has remarkably increased.

Being a Data Scientist in Austin offers the following benefits:

  • High pay
  • Multiple job opportunities
  • Tremendous job growth

Data Science is a very prominent field right now. Data Scientists get to enjoy a lot of perks and advantages compared to other jobs. Not only do they get to be in the proximity of upper level management due to their contributions in providing business insights to make better decisions, but they also get to work in any field of their interest. 

Cloudfare, XO Group and Pilytix are among the companies hiring Data Scientists in Austin.

Data Science Conferences in Austin, Texas

S.NoConference nameDateVenue
1.Percona Live 2019 Open Source Database Conference, Austin, TX28 May, 2019 to 30 May, 2019

Hyatt Regency Austin 208 Barton Springs Road Austin, TX 78704 United States

2.5th Annual - Data Center Austin Conference24 Sept, 2019 to 25 Sept, 2019

Brazos Hall 204 East 4th Street Austin, TX 78701 United States

3.The Business of Data Science - Austin
30 July, 2019 to 31 July, 2019

AT&T Executive Education and Conference Center 1900 University Ave Austin, TX 78705 United States

4.AI Insights: Users Group Conference 2019
21 Oct, 2019 to 24 Oct, 2019

AT&T Conference Center 1900 University Ave Austin, TX 78705

5.Data Engineering on Google Cloud Platform, Austin
20 May, 2019 to 23 May, 2019
Austin, TX
6.KNIME Fall Summit 2019 - Austin
5 Nov, 2019 to 8 Nov, 2019

AT&T Executive Education and Conference Center 1900 University Ave Austin, TX 78705 United States

7.5-Day Data Science Bootcamp in Austin
16 Sept, 2019 to 20 Sept, 2019

T&T Hotel and Convention Center 1900 University Ave Austin, Texas 78705 United States

8.Texas Scalability Summit
September 13, 2019

AT&T Executive Education & Conference Center 1900 University Avenue Austin, TX 78705 United States

9.Understanding the City of Austin through Data Visualization
May 17, 2019

City Hall, Board and Commissions Room 301 W 2nd St Austin, TX 78701 United States

10.The No-Limits Database Lunch & Learn, Austin
May 2, 2019

Omni Austin Hotel Downtown 700 San Jacinto Boulevard Austin, TX 78701 United States

1.  Percona Live 2019 Open Source Database Conference, Austin

  • About the conference: This conference is for the active open source data community and use open source database software to develop the business.
  • Event Date: 28 May, 2019 to 30 May, 2019
  • Venue: Hyatt Regency Austin 208 Barton Springs Road Austin, TX 78704 United States
  • Days of Program: 3
  • Timings: Tue, May 28, 2019, 8:00 AM – Thu, May 30, 2019, 6:30 PM CDT
  • Purpose: The purpose of the conference tackles subjects like architecture and design, security, scalability, analytics, performance, and operations.
  • Registration cost: $150 – $945
  • Who are the major sponsors: Percona

2. 5th Annual - Data Center Austin Conference, Austin

  • About the conference: The conference is about bringing creative people from the Data center industry together to collaborate, innovate, and motivate.
  • Event Date: 24 Sept. 2019 to 25 Sept. 2019
  • Venue: Brazos Hal, 204 East 4th Street, Austin, TX 78701, United States
  • Days of Program: 2
  • Timings: Tue, Sep 24, 2019, 8:00 AM – Wed, Sep 25, 2019, 11:00 PM CDT
  • Purpose: The purpose of the conference is to understand the needs of the advanced tech companies and equip themselves with technologies to meet their demands.
  • Registration cost: $475 – $800
  • Who are the major sponsors:  Data Center Austin Conference (DCAC)

3. The Business of Data Science, Austin

  1. About the conference: The seminar will help you harness the power of artificial intelligence and data science.
  2. Event Date: 30 July, 2019 to 31 July, 2019
  3. Venue: AT&T Executive Education and Conference Center 1900 University Ave Austin, TX 78705 United States
  4. Days of Program: 2
  5. Timings: Tue, Jul 30, 2019, 9:00 AM – Wed, Jul 31, 2019, 4:30 PM CDT
  6. Purpose: The purpose of the seminar is to teach the business leaders the fundamentals of data science and how to implement it in your organization.
  7. Registration cost: $1,725 – $2,190
  8. Who are the major sponsors: Pragmatic Institute

4. AI Insights: Users Group Conference 2019, Austin

  • About the conference: The conference aims at unlocking the value of data. The attendees will learn to collect, report, and integrate accurate data to make business decisions.
  • Event Date: 21 Oct, 2019 to 24 Oct, 2019
  • Venue: AT&T Conference Center 1900 University Ave Austin, TX 78705
  • Days of Program: 4
  • Timings: October 21, 2019 at 5:00 PM – October 24, 2019 at 12:00 PM (CDT)
  • Purpose: The purpose of the conference is to understand the data in context and make informed business decisions.
  • Registration cost: $279 - $ 349
  • Who are the major sponsors: American Innovations

5. Data Engineering on Google Cloud Platform, Austin

  1. About the conference: This four-day seminar will give an introduction to using the Google Cloud platform to design and build data processing systems.
  2. Event Date: 20 May, 2019 to 23 May, 2019
  3. Venue: Austin, TX
  4. Days of Program: 4
  5. Timings: May 20, 2019, 9:00 AM –May 23, 2019, 6:00 PM CDT
  6. Purpose: The purpose of the conference is to help the attendees deal with datasets, processing of data, querying datasets and visualizing the results.
  7. Whom can you Network with in this Conference: You will be able to network with experienced developers.
  8. Registration cost: $2,995
  9. Who are the major sponsors: ROI Training, Inc

6. KNIME Fall Summit 2019 - Austin

  • About the conference: The top data scientists and leaders will come together to learn about the KNIME software and how it can be used to solve problems related to data.
  • Event Date: 5 Nov, 2019 to 8 Nov, 2019
  • Venue: AT&T Executive Education and Conference Center 1900 University Ave Austin, TX 7870 United States
  • Days of Program: 4
  • Timings: Tue, Nov 5, 2019, 9:00 AM – Fri, Nov 8, 2019, 2:00 PM CST
  • Purpose: The purpose of the summit is to help the data scientists use the KNIME software to solve data problems in areas like retail sales, marketing, life sciences, manufacturing, etc.
  • Registration cost: $100 – $650
  • Who are the major sponsors: KNIME

7. 5-Day Data Science Bootcamp in Austin

  • About the conference: The conference will have about 4000+ aspiring data scientists who will be trained to use their data science skills in real-world applications.
  • Event Date: 16 Sept, 2019 to 20 Sept, 2019
  • Venue: AT&T Hotel and Convention Center 1900 University Ave Austin, Texas 78705 United States
  • Days of Program: 5
  • Timings: Mon, Sep 16, 2019, 8:00 AM – Fri, Sep 20, 2019, 6:00 PM CDT
  • Purpose: The purpose of the camp is to learn to work with big data and get training in solving real-world problems using data science.
  • Registration cost: $2,659.99 – $4,049.99
  • Who are the major sponsors: Data Science Dojo

8. Texas Scalability Summit, Austin

  • About the conference: The conference will cover distributed computing, schedulers, streaming data and processing and many more such concepts.
  • Event Date: September 13, 2019
  • Venue: AT&T Executive Education & Conference Center 1900 University Avenue Austin, TX 78705 United States
  • Days of Program: 1
  • Timings: 8:00 AM – 8:00 PM CDT
  • Purpose: The purpose of the conference is to help the attendees get information regarding different data concepts.
  • Registration cost: $265 – $495
  • Who are the major sponsors: Global Data Geeks

9. Understanding the City of Austin through Data Visualization, Austin

  • About the conference: The conference will help you understand the Data Science revolution through a couple of demo sessions.
  • Event Date: May 17, 2019
  • Venue: City Hall, Board and Commissions Room 301 W 2nd St Austin, TX 78701 United States
  • Days of Program: 1
  • Timings: 1:00 PM – 3:00 PM CDT
  •  Purpose: The purpose of the conference is to cover the different Machine learning techniques using tools like R/Python/Spark MLLIB.
  • How many speakers: 4
  • Speakers & Profile:
    • Flor Barajas
    • Stephanie Long
    • Thi Nguyen
    • Nathaniel Haefner
  • Registration cost: Free
  • Who are the major sponsors: Open Data Team, City of Austin

10. The No-Limits Database Lunch & Learn, Austin

  • About the conference: The conference will help you learn the transformation of data infrastructure and to help the organization deliver the new demands. The purpose of the conference is to move to Fast Data from Big data and make an ideal ecosystem.
  • Event Date: May 2, 2019
  • Venue: Omni Austin Hotel Downtown 700 San Jacinto Boulevard Austin, TX 78701 United States
  • Days of Program: 1
  • Timings: 11:30 AM – 1:30 PM CDT
  • Purpose: Giving information regarding the Data Science training in Hyderabad at the Kelly Technologies training institute.
  • Registration cost: Free
  • Who are the major sponsors: SME Solutions Group
S.NoConference nameDateVenue
1.AnacondaCON 2017, Discover What #OpenDataScience Means

7 February, 2017 - 9 February, 2017

JW Marriott Austin 110, East 2nd Street, Austin, TX 78701 Austin TX the US

2.MAC: Marketing Analytics Conference5 June, 2017 - 6 June, 2017

3.KNIME Fall Summit 2017
1 November, 2017 - 3 November, 2017
AT&T Executive Education and Conference Center
4.K-CAP 2017: Knowledge Capture
December 4th - 6th, 2017

The Hilton Garden Inn Austin Downtown/Convention Center, in, Austin, Texas

5.AnacondaCON 2018
8 April, 2018 - 11 April, 2018

JW Marriott Austin 110 E 2nd St.Austin, TX, 78701, United States

6.TEXATA Summit: The Data Analytics Conference of Texas
19 October, 2018

AT&T Hotel & Conference Center, Zlotnik Ballroom (Level M1) 1900 University Ave, Austin, TX 78705, USA

7.KNIME Fall Summit, learn about KNIME Analytics Platform
6 November, 2018 - 9 November, 2018

1. AnacondaCON 2017, Discover What #OpenDataScience Means, Austin

  • About the conference: The Conference had a discussion on Open Data Science. 
  • Event Date: 7 February, 2017 - 9 February, 2017
  • Venue: JW Marriott Austin, 110 East 2nd Street, Austin, TX 78701 Austin TX the US
  • Days of Program: 3
  • Timings: 6:00 PM to 2:30 PM
  • Purpose: The purpose of the conference was to understand the latest and upcoming trends in Open Data Science and learn the best practices to use Anaconda.

2. MAC: Marketing Analytics Conference, Austin

  • About the conference: The attendees learned the latest technologies in machine learning, attribution, cross-channel integration, segmentation, and microclusters.
  • Event Date: 5 June, 2017 - 6 June, 2017
  • Days of Program: 2
  • Purpose: The conference focused on three aspects of Data Science Automation, Attribution and Integration.
  • Who were the major sponsors:
    • AnalyticsIQ
    • CIVIS Analytics
    • Datorama
    • Socedo
    • Treasure Data
    • Wealthengine

    3. KNIME Fall Summit 2017, Austin

    • About the conference: It was an interactive conference where top data scientists discussed KNIME Software and its use to solve the complex data issues.
    • Event Date: 1 November, 2017 - 3 November, 2017
    • Venue: AT&T Executive Education and Conference Center 1900 University Ave, Austin, TX 78705, USA
    • Days of Program: 3
    • Timings: 9 A.M to 7 P.M.
    • Purpose: The purpose of this conference was to discuss the use of KNIME Software to solve data problems in various fields like marketing, retail sales, etc.

    4. K-CAP 2017: Knowledge Capture, Austin

    • About the conference: This conference focused on operating data from heterogeneous data sources through maintaining knowledge graphs. 
    • Event Date: December 4th-6th, 2017
    • Venue: The Hilton Garden Inn Austin Downtown/Convention Center, Austin, Texas
    • Days of Program: 3
    • Purpose: The conference provided a platform for researchers from different areas of Artificial Intelligence incorporating machine learning, knowledge acquisition, knowledge representation, text extraction, intelligent user interfaces, visualization, and similar technologies to promote integration, retrieval, and reuse of data.
    • Speakers & Profile:
      • Kenneth D. Forbus, Walter P. Murphy Professor of Computer Science and Professor of Education at Northwestern University.
      • Juan F. Sequeda, co-founder of Capsenta
    • Registration cost: early bird $450/ late $500/ on-site $600

    5. AnacondaCON 2018, Austin

    • About the conference: It brought together business leaders, developers, analysts, and IT professionals from around the world to share their insights into the latest technologies in data science.
    • Event Date: 8 April, 2018 - 11 April, 2018
    • Venue: JW Marriott Austin, 110 E 2nd St.Austin, TX, 78701, United States
    • Purpose: The purpose of the conference was to provide a platform for practitioners and innovators to discuss and share their opinions on the latest trends and technologies in data science used in science, research and business.

    6. TEXATA Summit: The Data Analytics Conference of Texas, Austin

    • About the conference: It was an annual conference that provided a platform for showcasing the latest ideas and research work in Artificial Intelligence, Machine Learning, Data Science and Big Data Analytics.
    • Event Date: 19 October, 2018
    • Venue: AT&T Hotel & Conference Center, Zlotnik Ballroom (Level M1), 1900 University Ave, Austin, TX 78705, USA
    • Days of Program: 1
    • Timings: 9:15 AM to 5:15 PM
    • Purpose: The purpose of this conference was to discuss various analytic tools and technologies used in Big Data and Advanced Analytics to provide solutions for challenges faced by the industry today.
    • Registration cost: $600

    7. KNIME Fall Summit, learn about KNIME Analytics Platform, Austin

    • About the conference: It was an interactive conference where top data scientists imparted knowledge on KNIME Software and its use to solve the complex data issues.
    • Event Date: 6 November, 2018 - 9 November, 2018
    • Days of Program: 4
    • Purpose: The purpose of this conference was to develop a better understanding of the KNIME Analytics Platform and its use in different areas like marketing, manufacturing, life sciences, etc.

    Data Scientist Jobs in Austin, Texas

    The best learning path to getting a job as a data scientist is as follows:

    1. Getting started: To get started in the field of data science, firstly you need to select a programming language that you are comfortable in. Python and R are the most commonly used programming languages by the data scientist. Also, you need to understand what is the meaning of data science and as a data scientist, what will be your roles and responsibilities.
    2. Mathematics: It is known that Data Science is all about collecting the data, making sense out of it, deciphering patterns and relationships, and visualizing them into a format that is easy to understand. To perform this, a data scientist must have good command in mathematics and statistics. Here are a couple of topics that need special attention:
      • Descriptive statistics
      • Inferential statistics
      • Linear algebra
      • Probability
    3. Libraries: The job of a data scientist includes preprocessing the data, plotting the structured data and applying machine learning algorithms to it. For this, certain libraries and packages are used. Some of the famous libraries are:
      • Ggplot2
      • Matplotlib
      • NumPy
      • Pandas
      • Scikit-learn
      • SciPy
    4. Data visualization: Once the analysis of the data is finished, the next step is to find patterns and make it as simple as possible for the non-technical members of the team. Graphs and charts are the most popular ways for data visualization. The libraries used for this task are:
      • Ggplot2 - R
      • Matplotlib - Python
    5. Data preprocessing: The data that is generated every day is mostly in an unstructured form. Because of this, before any analysis can be done on it, the data has to undergo some preprocessing to make it in a structured form. Variable selection and feature engineering are done for this preprocessing. Once it is completed, the data is available in a structured form ready for the analysis.
    6. ML and Deep learning: For every data scientist, having machine learning and deep learning skills on the CV is a must. When you are dealing with a huge amount of data, you need the help of deep learning algorithms. We suggest that you spend a few extra weeks on topics like CNN, RNN, and neural networks that will help you improve your ML and deep learning skills.
    7. Natural Language processing: When it comes to data analysis, data, present in the form of text, is processed and classified. To accomplish the task, a data scientist must have an in-depth knowledge of natural language processing.
    8. Polishing skills: If you want to exhibit your data science skills, you can participate in online competitions like Kaggle. Apart from this, you can also take on new projects that will help you explore and expand your data science skills.

    When you start looking for a job as a data scientist, you need to prepare yourself. Here are the 5 steps that will help you do the same:

    • Study: For the interview preparation, you should cover all important topics including:
      • Statistics
      • Statistical models
      • Probability
      • Understanding neural networks
      • Machine Learning
    • Meetups and conferences: Next, you need to start building your network and expanding your professional connections. The best way to do it is by attending data science conferences and tech meetups.
    • Competitions: You also need to test, implement and polish your data science skills. To do so, you can participate in online competitions like Kaggle.
    • Referral: Referrals have become the main source of finding the right job opportunities in the IT industry. Make sure that your LinkedIn profile is well-maintained and updated. Also maintain good professional ties in the industry.
    • Interview: Once you think you are ready, start giving the interviews. You might have to face rejection a couple of times but don’t get disheartened. Learn from the questions you could not answer in the interview and prepare well for the next one.

    In today’s world, tons of data is generated every second of the day. This has made the job of a data scientist all the more important. The data that is generated contains patterns and ideas that can be very helpful in advancing the interests of the business. It is the responsibility of a data scientist to extract the relevant information and gather insights that can benefit the business.

    Overall, the job of a data scientist is to analyze the data, discover patterns and inference relevant information. The data that is provided to the data scientist can be in structured as well as unstructured form. 

    Data Scientist Roles & Responsibilities:

    • The most basic role of a data scientist is to get the data that is relevant to the business from a large amount of structured as well as unstructured data provided to them.
    • The next step is taking the data extracted from the huge pile of data and organize and analyze it.
    • After this, to make sense of the data, machine learning techniques, tools, and programs are created.
    • Lastly, to gather insights and predict future outcomes, statistical analysis is performed on the data.

    The sexiest job of the 21st century, data scientist, comes with its own perks. The high demand and less number of data scientists available issue has lead to 36% higher base salaries than any other predictive analytics job. The earnings of a data scientist depend on the following 2 things:

    • Roles and responsibilities
      • Data analyst: $59,553/yr
      • Database Administrator: $71,822/yr
      • Data scientist: $102,740/yr
    • Type of company
      • Startups: Highest pay
      • Public: Medium pay
      • Governmental & Education sector: Lowest pay

    A Data Scientist must be skilled in Math, computer science and trend spotting. The responsibility of a data scientist includes deciphering large volumes of data, mining the relevant data, and analyzing the data to make predictions. The Data Science career path is as follows:

    Business Intelligence Analyst: The responsibility of a business intelligence analyst is to figure out how the business works and how it can be affected by market trends. They need to get a clear picture of the status of the business and where it stands in the environment. This is done by performing the analysis of the data.

    Data Mining Engineer: The job of a data mining engineer is to examine the data needs for the business. They could be hired permanently by the company or could work as a third party. Apart from examining the data, the job of a data mining engineer is the creation of a sophisticated algorithm that helps in the data analysis.

    Data Architect: The main responsibility of a Data Architect is working alongside developers, system designers, and users. It’s their job to create blueprints used for the integration, centralization, protection, and maintenance of the data sources. 

    Data Scientist: The main role of a Data Scientist is analyzing, developing hypotheses, developing an understanding of the data and exploring patterns from the given data in order to pursue the business case. The responsibilities of a data scientist include developing systems and algorithms that convert the raw data into insights that are productive and can be used to further the interest of the business.

    Senior Data Scientist: The responsibility of a senior data scientist is anticipating the future needs of the business. Once the needs of the business are identified, all the data analysis, systems, and future projects will be shaped to fulfill the needs of the business in the future.

    If you want to network with other data science professionals and become active in the data science community, you can join some organizations or groups. The top professional associations and groups for Data Scientists in Austin, TX are mentioned below – 

    To network with other data scientists in Austin, TX, you can try any of the following:

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

    The top 8 data science career opportunities in Austin, TX in 2019 are – 

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

    When employers look for a data scientist, they prefer them to have mastery over some skills. The University of Texas, Austin has a course in Data Science that will help you cover all the basic technical skills required to be a top-notch data scientist. Here are the tools and software that a data scientist must be an expert in:

    • Education: Data Scientist is a job that requires knowledge. You must have a degree in data science to get that knowledge. You can also get some certifications.
    • Programming: For every data scientist, programming knowledge is a must. Python is one such language. Before you start with the data science libraries, you need to go through Python basics.
    • Machine Learning: If you want to be a data scientist, you have to have machine learning and deep learning skills. This is required to find the relationship and analyze patterns in the data.
    • Projects: You must have a couple of real-world projects in your portfolio because it is the best approach to learn data science.

    Data Science with Python in Austin, Texas

    Python is considered the most popular language to learn data science due to the following reasons:

    • It is a multi-paradigm programming language, meaning the various facets of the language can be used in the field of data science. It is an object-oriented, structured programming language that comes with several packages and libraries. These can be useful while working in Data Science.
    • Python is a simple, readable and understandable language. Its large number of packages and analytical libraries attract so many data scientists.
    • Python comes with a broad and diverse range of resources that are available to the data scientist. These resources come in handy when a data scientist is stuck on a problem while creating a python program or a data science model.

    • Python is supported by its big, open-source community with many developers working on Python every day. So, if you are stuck in a problem, you can easily get help because it is quite possible that a developer has faced a similar problem before and found a solution for it. Even if it has not been addressed before, the Python community will try its best to help a fellow Python developer.

    Choosing a programming language can be a difficult task because you need one that can work well with data science and you are comfortable using. Here are the top 5 programming languages used in Data Science that you can go for:

    • R: R is considered a difficult language to learn. However, it is one of the most commonly used languages in data science. It is because of the following reasons:
      • There are a lot of statistical functions available that help in carrying out complex matrix operations smoothly.
      • With ggplot2, it offers great data visualization features.
      • It comes with a big open-source community that provides several high-quality, open source packages.
    • Python: It is the most sought after language in the field of data science, even though it has fewer packages than R. This is because of the following reasons:
      • It is easy to learn and implement.
      • It also comes with a big, open-source community.
      • Most of the libraries needed for data science can be provided by tensorflow, pandas, and scikit-learn.
    • SQL: To work with relational databases, you need to have knowledge of SQL or structured query language.
      • It has an easy to read and write syntax.
      • When it comes to updating, manipulation, and querying of databases, it is very efficient.
    • Java: Using Java in Data Science is not easy. There are not many libraries and the verbosity of the language has its limit. But, the language has certain advantages as well:
      • It is a very compatible language. There are many systems with backend code in Java. This makes the data science project easier to integrate with the system.
      • It is a general purpose, high-performance, and compiled language.
    • Scala: Scala has a complex syntax. Still, it is a preferred language in the field of data science because of the following reasons:

      • It runs on JVM that makes it compatible with Java
      • It can be used for high-performance cluster computing when used along with Apache Spark.

    This is how you can download and install Python 3 on windows:

    Download and setup: Head to the download page and install the python on Windows using a GUI installer. Make sure that you check the box asking for ass Python 3.x to PATH that will allow you to use the functionality of python from the terminal.

    You can also try using Anaconda for installing Python. To check the version of Python installed on your windows, you can use the following command:

    python --version

    • Update and install setuptools and pip: For the installation and update of most crucial libraries (3rd party), use the following command:

    python -m pip install -U pip

    Note: For creating an isolated Python environment and pipenv, you have to install virtualenv. Pipenv is a dependency manager for Python.

    To install Python 3 on Mac OS X, you can either directly use a .dmg package and install python from their official website or use Homebrew for the installation of Python and its dependencies. All you need to do is to follow these steps:

    1. Install Xcode: Before you install brew, you need to install the Xcode package of Apple. You need to start with the following command: $ Xcode-select --install
    2. Install brew: Next step is installing Homebrew which is Apple's package manager. You need to use the following command: /usr/bin/ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)" You can confirm if it is installed by using the command: brew doctor
    3. Install Python 3: The last step is installing Python. For that, use the following code:
      brew install python
      • You can confirm the version of Python installed on the computer: python --version

    For creating isolated spaces to run your projects, you can install virtualenv. This can also be used if you want to use different versions of Python in different projects.

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    FAQs

    The Course

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

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

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

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

    Tools and Technologies used for this course are

    • Python
    • MS Excel

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

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

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

    Finance Related

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

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

    The Remote Experience

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

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

    Have More Questions?

    Data Science with Python Certification Course in Austin, TX

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