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.
Learn from the best in the field. Our mentors are all experienced professionals in the fields they teach.
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).
In 2012, Harvard Business Review dubbed Data Scientist the sexiest job of the 21st Century. Companies like Google, Facebook collect user data and sell them to ad companies to earn crazy profits. How do you think they know whether you like dogs or cats? How do you think Amazon knows what products to recommend to you even when they haven’t explicitly asked you about it? The answer is data. Some other major reasons why data science is popular are:
Therefore, it’s in demand both from a company’s perspective and from an employee’s perspective
In Houston, colleges like The University of Texas, University of Houston, and Sam Houston State University offers online and offline courses in Data Science that can help you learn the skills required to be a top-notch Data Scientist. The top skills that are needed to become a data scientist include the following:
If you want to become a successful Data Science professional, you need to incorporate these 5 essential behavioral traits in yourself:
In Houston, TX, leading companies are looking for Data Scientists to help in optimizing their business including CGG, Tessella, BBVA Compass, Harris Health System, Hewlett Packard Enterprise, KPMG, Microsoft, Two Sigma Investments, LLC., GSI Environmental, Cardno. Etc. The 5 proven benefits of being a data scientist in Houston are:
If you want to become a data scientist, you must have these 4 business skills:
Before you get a job as a data scientist, you need to brush up on your data science skills. Here are the 5 best ways to do it:
In Houston, TX, all the major corporations are looking to harness the benefits of Data. The employers looking for Data Scientists include Harris Health System, Amazon Web Services, Two Sigma Investments, CGG, Tessella, BBVA Compass, Hewlett Packard Enterprise, KPMG, Microsoft, LLC., GSI Environmental, Cardno., ExxonMobil, Schlumberger, TGS, McDermott, Pros., Noble Energy, Inc, David Weekley Homes, Drillinginfo, etc.
The best way to practice your data science skills is by solving data science problems, for which there are several problems available online. Here we have listed a few of them,categorized according to their difficulty level and your expertise level.
If you want to become a top-notch data scientist, you need to follow the below mentioned steps:
Here, we have compiled a list of steps required to become a Data Scientist:
Getting a degree in Data Science is very important to help land a job as a Data Scientist. About 88% of data scientists have a Master's degree and 46% have PhDs. There are many universities in Houston offering Data science courses, including Sam Houston State University, University of Houston, The University of Texas, etc. The reasons why it is so important include:
A degree is very important because of the following –
You can grade yourself on the scorecard below and determine if you should go for a Master's degree or not. If your total score is more than 6 points, we recommend a Master's degree:
Programming is the most basic and important skill that you can have as a data scientist. Here is why programming knowledge is a must to become a data scientist:
If you want to get a job as a data scientist, you need to follow the given logical sequence of steps:
While preparing for the job of a data scientist, here are the 5 important steps that you need to follow:
The main responsibility of a data scientist is to analyze the data to decipher patterns and relationships and use this information to meet the needs and goals of the business. This data is available in the raw form, which can be unstructured as well as structured.
With tons of data generated every minute, the job of a data scientist has become more important than ever. This data is a goldmine of information that can help in the advancement of a business. It is up to the data scientist to extract the insights from the huge pile of data and benefit the business. The roles and responsibilities of a data scientist include:
Data Scientist Roles & Responsibilities:
Because of high demand and less number of data scientist’s issue, there has been an increase in a 36% increase in base salaries of data scientists that is significantly higher than any other predictive analytics professionals. The pay of a data scientist depends on the following two things:
To be a successful data scientist, one must be skilled in Mathematics, computer science, and trend spotting. It is the responsibility of a data scientist to analyze the large volumes of data to make predictions for the future. The career path of a data scientist is as follows:
Business Intelligence Analyst: To figure out the needs of the business and market trends, a business intelligence analyst is required. To develop a clear picture of the current standing of the business in the business environment, analysis is done as a part of this job.
Data Mining Engineer: A Data Mining Engineer is responsible for the examination of data required to fulfill the needs of the business. They might be hired by the company as a full-time employee or a third party. Apart from examining the data, the job of a Data Mining Engineer also involves the creation of a sophisticated algorithm that helps in further analysis of data.
Data Architect: Data Architects work alongside System developers, designers, and users for creating blueprints. These blueprints are then used by the data management system that integrates, centralizes, maintain and protect the data sources.
Data Scientist: The job of a Data Scientist is to analyze the business case, develop a hypothesis and an understanding of data. They are also responsible for developing systems and algorithms that use this data in a productive manner to further the interests of the business.
To network with other data scientists in Houston, TX to potentially fill data scientist employees in a team, you can try visiting one of the following:
The top 8 Data Science Career opportunities in Houston in 2019 are–
To get a job as a data scientist, you need to mastery over some tools and software including the following:
When it comes to data science, choosing an appropriate language that is fit for the field and you are comfortable working in is important. It is a huge field and you need multiple libraries to carry out the work in a smooth way. Here are the 5 most popular languages used by the data scientists worldwide:
If you want to download and install Python 3 on Windows, you need to follow these steps:
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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.
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Minimum Requirements: MAC OS or Windows with 8 GB RAM and i3 processor