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.
Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn
Understand the behavior of data;build significant models using concepts of Statistics Fundamentals
Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel
Use Python libraries and work on data manipulation, data preparation and data explorations
Use of Python graphics libraries like Matplotlib, Seaborn etc.
ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees
There are no prerequisites to attend this course, but elementary programming knowledge will come in handy.
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Learn theory backed by practical case studies, exercises and coding practice. Get skills and knowledge that can be effectively applied.
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Learn concepts from scratch, and advance your learning through step-by-step guidance on tools and techniques.
Get reviews and feedback on your final projects from professional developers.
Get an idea of what data science really is.Get acquainted with various analysis and visualization tools used in data science.
Hands-on: No hands-on
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.
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.
Write python code to formulate Hypothesis and perform Hypothesis Testing on a real production plant scenario
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.
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.
Understand Time Series Data and its components like Level Data, Trend Data and Seasonal Data.
Work on a real- life Case Study with ARIMA.
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.
Project to be selected by candidates.
With attributes describing various aspect of residential homes, you are required to build a regression model to predict the property prices.
This project involves building a classification model.
Predict if a patient is likely to get any chronic kidney disease depending on the health metrics.
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).
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:
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:
The top 5 essential behavioral traits of a successful Data Science professional include:
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:
The top 4 must-have business skills required to become a data scientist include:
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:
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:
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:
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:
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:
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:
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:
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:
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.
|1.||Percona Live 2019 Open Source Database Conference, Austin, TX||28 May, 2019 to 30 May, 2019|
Hyatt Regency Austin 208 Barton Springs Road Austin, TX 78704 United States
|2.||5th Annual - Data Center Austin Conference||24 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
2. 5th Annual - Data Center Austin Conference, Austin
3. The Business of Data Science, Austin
4. AI Insights: Users Group Conference 2019, Austin
5. Data Engineering on Google Cloud Platform, Austin
6. KNIME Fall Summit 2019 - Austin
7. 5-Day Data Science Bootcamp in Austin
8. Texas Scalability Summit, Austin
9. Understanding the City of Austin through Data Visualization, Austin
10. The No-Limits Database Lunch & Learn, Austin
|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 Conference||5 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
2. MAC: Marketing Analytics Conference, Austin
3. KNIME Fall Summit 2017, Austin
4. K-CAP 2017: Knowledge Capture, Austin
5. AnacondaCON 2018, Austin
6. TEXATA Summit: The Data Analytics Conference of Texas, Austin
7. KNIME Fall Summit, learn about KNIME Analytics Platform, Austin
The best learning path to getting a job as a data scientist is as follows:
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:
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 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:
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 –
The top 8 data science career opportunities in Austin, TX in 2019 are –
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:
Python is considered the most popular language to learn data science due to the following reasons:
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:
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 -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:
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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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.
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
There are no restrictions but participants would benefit if they have basic programming knowledge and familiarity with statistics.
Yes, KnowledgeHut offers virtual training.
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.
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.
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