Conditions Apply

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

Learn to analyze data with Python in this Data Science with Python comprehensive course.

  • 42 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
View Schedules Contact Course Advisor
Corporate Training
Refer and earn

Online Classroom (Weekday)

Mar 29 - Apr 26 06:00 PM - 08:00 PM ( CDT )

USD 2199

USD 649

Online Classroom (Weekday)

Mar 29 - Apr 26 07:00 PM - 09:00 PM ( CDT )

USD 2199

USD 649

CITREP+ funding support is eligible for Singapore Citizens and Permanent Residents

Know More

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 analyzing, 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 interview prep will help you land lucrative jobs.

What You Will Learn

1. Tools & Technologies

Get acquainted with various analysis and visualization tools such as Matplotlib and Seaborn

2. Statistics for Data Science

Understand the behavior of data;build significant models using concepts of Statistics Fundamentals

3. Python for Data Science

Learn the various Python libraries to manipulate data, like Numpy, Pandas, Scikit-Learn, Statsmodel

4. Exploratory Data Analysis

Use Python libraries and work on data manipulation, data preparation and data explorations

5. Data Visualization using Python

Use of Python graphics libraries like Matplotlib, Seaborn etc.

6. Advanced Statistics & Predictive Modeling

ANOVA, Linear Regression using OLS, Logistic Regression using MLE, KNN, Decision Trees

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

Know More

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.

Certification Process

What is Data Science
Data Scientist Skills & Qualifications
How to become a Data Scientist in Houston, TX
Data Scientist Jobs in Houston, TX
Data Science with Python Houston, TX

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.

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

What is Data Science

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:

  • Data-driven decision making is increasing in demand. 
  • Due to the lack of well-trained data scientists, professionals trained in data science are offered the highest salary in the tech world.
  • Data is being collected at an exceptionally high rate, which requires an equal rate of analysis to make the most of it. Data scientists can help a company take crucial marketing decisions based on their findings from raw data. 

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:

  1. Python Coding: Python is the most popular programming language used in Data Science. It is simple, versatile, can take different data formats and aid in the processing of data. It also helps in creating datasets and performing operations on them.
  2. R Programming: If you want to become a data scientist, you need to have knowledge of an analytical tool. This is where the R language comes into play. It is a go-to programming language if you want to make your data science problems easier to solve.
  3. Hadoop Platform: Although not a must, in-depth knowledge of the Hadoop platform is recommended as it is used in several data science projects. A study recently revealed that Hadoop is one of the leading skill requirements for a job as a data scientist.
  4. SQL database and coding: SQL is a database language used for accessing, working, and communicating the data. With this, a data scientist can gain insights into the formation and structure of the database. MySQL is another such language with concise commands through which you can perform operations using less technical skills in less time.
  5. Machine Learning and Artificial Intelligence: It is a prerequisite to be in the field of data science. Knowledge of Machine Learning and Artificial Intelligence will help you analyze the data and use this data to gain insights. You should be familiar with topics like a neural network, reinforced learning, logistic regression, machine learning algorithms, decision, tress, adversarial learning, etc. 
  6. Apache Spark: Apache Spark is a popular data sharing technology used for big computation. It is quite similar to Hadoop, except it is faster than Hadoop. This is because Sparks use system memory to cache its computation whereas Hadoop reads and writes to the disk. Therefore, it can be used to run the algorithms faster. It is a great framework while working with large datasets and handling complex unstructured data. Some other benefits of using Apache Spark include prevention of data loss, faster speed, and ease of carrying out operations.
  7. Data Visualization: It is the responsibility of a data scientist to visualize the results obtained after a series of complicated processes performed on the data in a format easily understandable by everyone. There are tools available to help with a visualization like ggplot, d3.js, matplotlib, and Tableau. It also helps in getting a quick insight and enabling into the new outcome.
  8. Unstructured data: Most of the data that we have is unstructured, unlabelled and cannot be organized into database values. Some examples of these unstructured data include blog posts, audios, videos, social media posts, customer reviews, etc.

If you want to become a successful Data Science professional, you need to incorporate these 5 essential behavioral traits in yourself:

  • Curiosity – If you want to be able to deal with such a huge amount of data, you must be curious and have an undying thirst for knowledge.
  • Clarity – If you are constantly looking for clarity by asking questions like ‘why’ or ‘so what’, data science is the field for you. You should know what you are doing and when you are doing, whether you are writing code or cleaning up data.
  • Creativity – A data scientist needs creativity for developing new modeling features, creating new tools and developing new ways for data visualization.
  • Skepticism – Skepticism is as important as creativity for the job of a data scientist. It is required to stay in the real world and not get carried away with creativity.

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:

  1. High Pay: Since there is a high demand of data scientists right now and the number of experienced data scientists is low, data scientist jobs have become one of the highest paying jobs in the tech world.
  2. Good bonuses: With the handsome pay, the job of a data scientist comes with signing perks, equity shares, and impressive bonuses.
  3. Education: To become a data scientist, you need to earn a Master's degree or a Ph.D. that opens up doors to work as a researcher or lecturer in government or a private institution.
  4. Mobility: A job of a data scientist can increase your standard of living as most of the organizations that collect data are located in developed countries.
  5. Network: Being a data scientist will involve publishing research papers, attending conferences, tech talks, and meetups that will help you network. This will help you for referral purposes in the future.

Data Scientist Skills & Qualifications

If you want to become a data scientist, you must have these 4 business skills: 

  1. Analytic Problem-Solving – The first step to finding a solution to a problem is to understand and analyze the problem. You need to have a clear perspective to develop the right strategies required to solve the problem.
  2. Communication Skills – Communicating skills are very important as it helps the data scientists explain deep business and customer analytics to the companies.
  3. Intellectual Curiosity: To be a good data scientist, you need to be curious and have a thirst to produce results that will boost the value of your commercial enterprise.
  4. Industry Knowledge – A good data scientist has a solid knowledge of the industry he/she is working in. This will help you in analyzing the data as you will know what is important and what is not.

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:

  • Boot camps: Lasting for about 4 to 5 days, boot camps are a great way to brush up your basics. They help you get theoretical knowledge as well as practical hands-on experience.
  • MOOC courses: MOOC are the online courses taught by data science experts that help you stay updated with the latest trends in the industry and polish your implementation skills through multiple assignments.
  • Certifications: With a certification, you will have improved your CV significantly and added an additional skill set. 
  • Projects: When it comes to brushing up your skills, projects are the best way to do it. The more you work, the more refined your skills will be. You can either work on an existing project or take on a new one.
  • Competitions: You can participate in online competitions like Kaggle that can improve your problem-solving skills. During the competition, you will have to find a solution to a problem with certain restraints and satisfy all the requirements.

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.

  • Beginner Level
    • Iris Data Set: The Iris Data Set contains 4 columns and 50 rows which are perfect for a beginner. It is a popular, resourceful, easy, and versatile dataset that uses pattern recognition. With this data, you will be able to learn the different classification techniques and start your journey in the Data Science field.Practice Problem: The problem is to predict the flower’s class using these parameters.
    • Bigmart Sales Data Set: The Retail sector is an industry that uses analytics for optimizing their business processes. While solving the problem, you will deal with retail concepts like product bundling, inventory management, customizations, etc. All of these can be handled using business analytics and data science. It is a regression problem consisting of 12 columns and 8523 rows.Practice Problem: The problem is to predict the total sales of the retail store.
  • Intermediate Level:
    • Black Friday Data Set: This dataset consists of sales transactions made in a retail store. This dataset is the best as it helps you explore your engineering skills while giving you an understanding of how millions of customers shop daily. It is a regression problem with 12 columns and 550,069 rows.Practice Problem: The problem is to predict the total purchase amount.
    • Text Mining Data Set: The Text Mining Data Set contains aviation safety reports describing the issues encountered during certain flights. This data set was obtained in 2007 during the Siam Text Mining Competition. It is a high-dimensional and multi-classification problem containing 30,348 rows and 21,519 columns.Practice Problem: The problem is the classification of the documents on the basis of their labels.

  • Advanced Level:
    • Identify the digits data set: Comprising of 7000 images with dimensions of 28X28 each, this dataset involves studying, analyzing and recognizing different elements present in an image.Practice Problem: The problem is the identification of the elements present in the image.
    • Vox Celebrity Data Set: This large scale identification problem is very important in the arena of deep learning using audio processing. The dataset contains 100,000 words spoken by 1,251 celebrities extracted from YouTube videos. It can help you understand the process of isolating and identifying speech.Practice Problem: The problem is to identify the voice of the celebrity.

How to Become a Data Scientist in Houston, Texas

If you want to become a top-notch data scientist, you need to follow the below mentioned steps:

  1. Getting started: The first step is to select a programming language to work with. We recommend that you pick either Python or R as they are the most popular languages used in the field of Data Science.
  2. Mathematics and statistics: A good data scientist must have a good grasp of basic algebra and statistics. You will need them while dealing with data, discovering patterns and relationships.
  3. Data visualization: Learning to visualize the data is an important step in becoming a data scientist. You need it for better communication with the end users and in helping the non-technical members of the team understand the content as well.
  4. ML and Deep learning: Every data scientist must be an expert in Deep Learning as well as Machine Learning. It helps you to analyze the data.

Here, we have compiled a list of steps required to become a Data Scientist:

  1. Degree/certificate: To be a data scientist, you need to have a degree in Data Science. You need to get started with a course that covers all the fundamentals. This course can be online or offline, depending on what suits you. During the course, you will be learning the application of cutting-edge tools. This is a tough job that demands continuous learning due to the rapid advancements in the field. You can also try getting certifications that will improve your CV significantly.
  2. Unstructured data: Tons of data is generated every day. Most of this data is in an unstructured format. It is the job of a data scientist to deal with this unstructured data and discover patterns in it. This makes the job more complex as a lot of work is required to structure the data.
  3. Software and Frameworks: Frameworks play an essential role in data science. When used with a programming language like Python or R, they help in structuring the data and analyzing it. 
    • R language has a steep learning curve. Still, it is one of the most used programming languages in Data Science. They have a lot of statistical functions that help in data analysis. About 43% of Data Scientist perform their data analysis using R.
    • Hadoop is a framework that is used by data scientists in situations where, compared to memory at hand, the available data is in excess. The framework conveys the data to different points on the machine. Spark is another popular framework. Used for computational purposes, Spark is faster than Hadoop. It can also prevent data loss.
    • Once you have mastered the programming languages and the framework, you can move on to the databases. A good data scientist must have an in-depth knowledge of SQL queries.
  4. Machine learning and Deep Learning: Once the data has been collected and prepared, you need to apply machine learning and deep learning algorithms to analyze the data. The Data Science models are trained to deal with the data that is provided.
  5. Data visualization: Data visualization is a very important skill for a data scientist. It helps them make an informed business decision after carefully analyzing the data. The data must be presented in the form of charts and graphs. There are several visualization tools available for this purpose including ggplot2, matplotlib, etc.

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 – 

  • Networking – When you are pursuing a degree, you can start building your network by making friends and acquaintances. It will help you a lot later.
  • Structured learning – When you are enrolled in a course, you have to follow a schedule and keep up with the curriculum. This structured learning is effective and beneficial.
  • Internships – During your internship, you will get practical hands-on experience.
  • Recognized academic qualifications for your résumé – A degree from a prestigious institution will look good on your CV and help you land a better job.

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:

  • You have a strong STEM (Science/Technology/Engineering/Management) background: 0 point
  • You have a weak STEM background ( biochemistry/biology/ economics or another similar degree/diploma): 2 points
  • You are from a non-STEM background: 5 points
  • You have less than 1 year of experience in working with Python programming language: 3 points
  • You have never been part of a job that requires you to code on a regular basis: 3 points
  • You think you are not good at independent learning: 4 points
  • You do not understand when we tell you that this scorecard is a regression algorithm: 1 point

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:

  • Data sets: While working in the field of data science, you will have to deal with large datasets. To analyze this huge amount of data, you will need the help of programming.
  • Statistics: Knowledge of statistics will be of no use if the data scientist doesn’t know how to program to implement it.
  • Framework: With the knowledge of programming, a data scientist will be able to build a framework that can analyze experiments automatically, manage the data pipeline and visualize data.

Data Scientist Jobs in Houston, Texas

If you want to get a job as a data scientist, you need to follow the given logical sequence of steps:

  1. Getting started: First things first, you need to select a programming language that can be used in Data Science and you are comfortable working in. The most preferred languages by data scientists are Python and R. You also need to understand what are the roles and responsibilities of a data scientist.
  2. Mathematics: If you want to make sense of raw data, decipher patterns and find relationships, you need to have a good command over mathematics and statistics. You need to pay special attention to a few topics like:
    • Descriptive statistics
    • Inferential statistics
    • Linear algebra 
    • Probability
  3. Libraries: Data Science includes processes like preprocessing the data, plotting the structured data, applying machine learning algorithms to this data. To accomplish these tasks, libraries are required. Some of the famous libraries are mentioned below:
    • Ggplot2
    • Matplotlib
    • NumPy
    • Pandas
    • Scikit-learn
    • SciPy
  4. Data visualization: It is the responsibility of a data scientist to make the data as simple as possible so that the non-technical members of the team can understand it as well. You can try creating graphs and charts. To accomplish this, there are certain tools available:
    • Ggplot2 - R
    • Matplotlib - Python
  5. Data preprocessing: Most of the data that we have is in unstructured form. To make it ready for analysis, a data scientist has to preprocess it. This is done using variable selection and feature engineering. Once the preprocessing is done, the data is injected into the machine learning tool for analysis.
  6. ML and Deep learning: For a data scientist, proficiency in deep learning and machine learning is important. Deep learning is essential while dealing with huge data sets while machine learning is required for data analysis. You need to be well versed with tops like CNN, RNN, and Neural networks.
  7. Natural Language processing: Proficiency in Natural Language Processing is important because it involves data classification and processing of textual data.
  8. Polishing skills: If you want to polish and exhibit your data science skills, you can go for online competitions like Kaggle. Apart from this, you can create your own projects and explore the field of data science.

While preparing for the job of a data scientist, here are the 5 important steps that you need to follow:

  • Study: You need to cover all the important topics while preparing for the interview. Here are some important topics:
    • Machine Learning 
    • Probability
    • Statistics
    • Statistical models
    • Understanding neural networks
  • Meetups and conferences: Go build your professional network and expand your connections by meeting other data science professionals in tech meetups and conferences.
  • Competitions: Participate in online competitions like Kaggle for implementing, testing, and polishing your skills. 
  • Referral: Update your LinkedIn profile and find someone who can refer you. Referrals are the primary source of interviews in the tech world and will help you land the job.
  • Interview: If you think you are ready, go for the interview. You might have to go through a couple of bad interviews before you get a job. Just learn from each interview and get answers to questions you couldn’t answer in the interview.

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:

  • Getting the relevant data from the huge pile of structured and unstructured data provided to them by the organization.
  • Organizing and analyzing the data.
  • To make sense of the data, creating machine learning techniques, tools, and programs.
  • Performing statistical functions on relevant data for predicting future outcomes.

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:

  • Type of company
    • Governmental & Education sector: Lowest pay 
    • Public: Medium pay 
    • Startups: Highest pay 
  • Roles and responsibilities
    • Data analyst: $55,125/yr
    • Database Administrator: $80,111/yr
    • Data scientist: $123,086/yr

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.

There are several professional groups and associations created for data scientists for networking and discussing data science including:

  • Houston Data Science
  • Flatiron School Houston
  • North Houston Data Analytics and Machine Learning
  • Open Source Data Science
  • Data Natives Houston

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:

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

The top 8 Data Science Career opportunities in Houston in 2019 are– 

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

To get a job as a data scientist, you need to mastery over some tools and software including the following:

  • Education: Data Scientists is one of the jobs that require you to have a Ph.D. A degree from a prestigious institution will not only look good on your CV but will help you get comprehensive knowledge required to manage and analyze unstructured data. You can also try getting certifications that will add to your skills. 
  • Programming: Being proficient in programming is a must to be a data scientist. You need to cover your basics before moving on to any data science library.
  • Machine Learning: Having deep learning and machine learning skills is a must to analyze the pattern and find a relationship.
  • Projects: The best way to learn data science to take on real-world data science projects that will also build your portfolio.

Data Science with Python Houston, Texas

  • Being a multi-paradigm programming language makes Python one of the most common and popular languages used by data scientists. It has multiple facets that help the data scientists in their projects. This structured, object-oriented programming language has several libraries and packages useful for data science purposes.
  • Python is simple and readable. This makes it the most preferred programming language used by the data scientists. It comes with customized packages and libraries perfectly suitable for the field of data science.
  • If you ever get stuck in a python code or while building a data science model using python, there is a broad and diverse range of resources that can help you get out of it. All these resources are available at the disposal of a data scientist.
  • Using python comes with a big advantage, the support of the vast python community. Python is a popular language used by millions of developers worldwide. So, if you get stuck somewhere, there is a huge chance that someone has been stuck there before and found a solution for it. And if your problem is new, the helpful python community will try to find a solution for you.

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:

  • R: R is considered a difficult language because of its steep learning curve. However, it comes with certain advantages that make it one of the most used programming languages in Data Science:
    • Comes with several statistical functions that aid in data analysis.
    • It can handle matrix operations smoothly.
    • With ggplot2, R acts as a great tool for data visualization.
    • There is a big open source R community that offers several open source packages.
  • Python: Python is the most commonly used and preferred language in the field of data science. Even though it offers less number of packages than R, the following advantages cover for it:
    • Python is easy to learn and implement.
    • Pandas, scikit-learn, and tensorflow cover most of the libraries needed by data scientists
    • There is a big, open-source community for Python as well.
  • SQL: SQL is a Structured Query Language that works on relational databases.
    • The syntax of the language is pretty easy to read.
    • Updating, querying, and manipulating data is very easy using SQL in relational databases.
  • Java: Java offers fewer libraries than other programming languages and its verbosity limits its potential. Still, it is used in many data science projects due to the following reasons:
    • It is a high-performance, compiled, and general purpose language.
    • There are systems with backend code in Java. So, it is easy to integrate java data science projects to it.
  • Scala: Even though it has a complex syntax, it is a preferred language by data scientists due to the following reasons:
    • It runs on JVM. That makes it compatible with Java as well.
    • If used with Apache Spark, you can get high-performance cluster computing.

If you want to download and install Python 3 on Windows, you need to follow these steps:

  • Download and setup: First, visit the download page and through the GUI installer, start installing python on the windows. While installing, make sure that select the checkbox that asks you to add Python3.x to PATH, the classpath. This will allow using the functionalities of python directly from the terminal.

reviews on our popular courses

Review image

The content was sufficient and the trainer was well-versed in the subject. Not only did he ensure that we understood the logic behind every step, he always used real-life examples to make it easier for us to understand. Moreover, he spent additional time to let us consult him on Data Science-related matters outside the curriculum. He gave us advice and extra study materials to enhance our understanding. Thanks, KnowledgeHut!

Ong Chu Feng

Data Analyst
Attended Data Science with Python Certification workshop in January 2020
Review image

KnowledgeHut is a great platform for beginners as well as experienced professionals who want to get into the data science field. Trainers are well experienced and participants are given detailed ideas and concepts.

Merralee Heiland

Software Developer.
Attended PMP® Certification workshop in May 2018
Review image

I am glad to have attended KnowledgeHut's training program. Really I should thank my friend for referring me here. I was impressed with the trainer who explained advanced concepts thoroughly and with relevant examples. Everything was well organized. I would definitely refer some of their courses to my peers as well.

Rubetta Pai

Front End Developer
Attended PMP® Certification workshop in May 2018
Review image

The customer support was very interactive. The trainer took a very practical oriented session which is supporting me in my daily work. I learned many things in that session. Because of these training sessions, I would be able to sit for the exam with confidence.

Yancey Rosenkrantz

Senior Network System Administrator
Attended Agile and Scrum workshop in May 2018
Review image

The instructor was very knowledgeable, the course was structured very well. I would like to sincerely thank the customer support team for extending their support at every step. They were always ready to help and smoothed out the whole process.

Astrid Corduas

Telecommunications Specialist
Attended Agile and Scrum workshop in May 2018
Review image

Trainer really was helpful and completed the syllabus covering each and every concept with examples on time. Knowledgehut staff was friendly and open to all questions.

Sherm Rimbach

Senior Network Architect
Attended Certified ScrumMaster (CSM)® workshop in May 2018
Review image

I was totally impressed by the teaching methods followed by Knowledgehut. The trainer gave us tips and tricks throughout the training session. The training session gave me the confidence to do better in my job.

Matteo Vanderlaan

System Architect
Attended Certified ScrumMaster (CSM)® workshop in May 2018
Review image

I would like to extend my appreciation for the support given throughout the training. My trainer was very knowledgeable and I liked his practical way of teaching. The hands-on sessions helped us understand the concepts thoroughly. Thanks to Knowledgehut.

Ike Cabilio

Web Developer.
Attended Certified ScrumMaster (CSM)® workshop in May 2018

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.

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. 

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?

Mail Us support@knowledgehut.com

Data Science with Python Certification Course in Houston, TX

A city that has launched a thousand rockets, perhaps Houston?s greatest claim to fame is the presence of the NASA space centre. But there is lot more to this dynamic city than high-powered government offices and institutions. Home to some of the greatest museums, art deco architecture buildings, shopping districts and culinary delights, the city has something for everyone. Also present here is the Texas Medical Center which has the world's largest concentration of healthcare and research institutions and several trade, mining, engineering, and international companies. Consistently ranked as among the best places in the U.S to do business, Houston is a great place to study and work in. You can chose among several of KnowledgeHut?s courses to start your career here. These courses are globally recognized and will help you get the right footing. Courses include PRINCE2, PMP, PMI-ACP, CSM, CEH, Big Data, Hadoop, Python, Data Analysis, Android Development and much more. Note: Please note that the actual venue may change according to convenience, and will be communicated after the registration.

other training