Data Science Training in Houston

Top rated Data Science Training/Bootcamp in Houston

SynergisticIT offers the most interactive, highly engaging and Top rated Data Science training/Bootcamp in Houston. It is curated for those tech aspirants who want to learn Data Science from the ground level.

Thus, this training is a perfect learning path for freshers with zero knowledge of Data Science or professionals who looks to reinforce their Data Science skills. Our structured and career-focused curriculum lets you master the most in-demand Data Science skills required for a successful career. If you want to launch a Data Science career in a short span of 5 to 6 months, consider taking our comprehensive Data Science Job Placement program. 

Houston, Texas is known for its energy sector, healthcare institutions, financial services, and growing technology ecosystem. With industries increasingly relying on data-driven decision-making, the demand for data scientists, ML/AI engineers, data analysts, and data engineers has surged. Companies such as Shell, ExxonMobil, Chevron, Halliburton, Schlumberger, Baker Hughes, Phillips 66, ConocoPhillips, Sysco, MD Anderson Cancer Center, Baylor College of Medicine, Houston Methodist, Amazon, Microsoft, IBM, Accenture, Deloitte, Oracle, Cisco Systems, Verizon, AT&T, Wells Fargo, Bank of America, JPMorgan Chase, and Capital One are actively hiring professionals with advanced data skills in Houston.

 

Data science and machine learning (ML/AI) are transforming industries by enabling organizations to analyze massive datasets, uncover insights, and automate decision-making. Data science as a discipline has been evolving for more than 20 years, built on foundations of statistics, mathematics, and computer science. Today, it is future-proof because every industry—from healthcare to energy—relies on data-driven strategies.

Even with the rise of AI tools, data science remains indispensable. AI frameworks, cloud-native applications, and enterprise systems often integrate with data science pipelines. Employers are increasingly seeking professionals who can design algorithms, build predictive models, and deploy AI-powered solutions.

Why choose SynergisticIT for Data Science Training in Houston ?

  • SynergisticIT is a Top rated Data Science training/ Bootcamp in Houston.

  • We have a world-class Data Science faculty with 15+ years of industry experience.

  • Our seasoned instructors assist each student in live project development.

  • Over the years, we have built a solid association with Fortune 500 Companies like Cisco, PayPal, Apple, Google, IBM, Deloitte, and others that facilitate us to market our candidates’ skills.

  • We don’t impose any additional charges for repeating a session.

Data Science Training Online in Houston
  • You get lifetime access to industry-relevant and updated course material.

  • Most candidates take our Data Science training in Houston due to our higher placement rate of 97.8%. We provide career coaching and prepare our candidates for high-tech interviews through technical mock tests, psychological assessments, and behavioural questions.

  • Our immersive Data Science training acquaints candidates will all the necessary skills needed to commence a data-driven career.

  • We have dedicated instructors that closely monitor your performance to ensure you are job-ready.

  • We also prepare our candidates to build resumes and work portfolios as per the market standards.

  • Our candidates gain real-world experience working on hands-on exercises and case studies.

  • By the end of our Data Science training, you will be rewarded with a well-recognized certificate that can keep you ahead of the competition.

An Insight into Our Data Science Training

The curriculum of our Data Science training in Houston is centered around the core and advanced Data Science principles. It starts from the introduction to Data Science and moves forward to advanced concepts such as Data Manipulation, Data Visualization, Artificial Intelligence, Machine Learning, Decision Tree, Predictive Modeling, NLP, Web Scraping, etc.

 

Many coding bootcamps offer fragmented training, focusing only on one area such as data science or analytics. This forces jobseekers to attend multiple bootcamps to gain a complete skill set, which is costly and inefficient.

That’s why SynergisticIT’s Data Science Job Placement Program (JOPP) is different. With over 15 years in the tech industry, SynergisticIT understands employer expectations and offers one comprehensive program that integrates data engineering, data analytics, ML/AI, and data science fundamentals. Instead of juggling 4–5 separate bootcamps, jobseekers can enroll in one complete program that covers everything employers demand.

You can explore the SynergisticIT Job Placement Program and the SynergisticIT Data Science JOPP to see how these programs combine advanced training with real staffing support.

Tech Stack in the Data Science JOPP

The curriculum includes:

  • Data Science Fundamentals
  • Machine Learning & AI (LLMs, Generative AI, TensorFlow, PyTorch)
  • Data Engineering (Snowflake, Databricks, PySpark, Hadoop)
  • Data Analytics (Power BI, Tableau, SQL, SAS)
  • Projects & Certifications
  • MLOps Tools: Docker, Kubernetes, MLflow
  • Cloud Platforms: AWS, Azure, GCP

This holistic stack ensures candidates are job-ready across multiple domains.

Introduction to Data Science with Python

  • What is Data Science & Analytics?
  • Common Terms in Analytics
  • What is Data & its Classification?
  • Relevance in industry and need of the hour
  • Types of problems and business objectives in various industries
  • Critical success drivers
  • Overview of analytics tools & their popularity
  • Analytics Methodology & problem-solving framework
  • List of steps in Analytics projects
  • Build Resource plan for analytics project
  • Finding the most appropriate solution design for the given problem statement
  • Project plan for Analytics project & key milestones based on effort estimates
  • How leading companies are harnessing the power of analytics?
  • Why Python for data science?

Python Introduction & Data Structures

  • Python Tools & Technologies
  • Benefits of Python
  • Important packages (Pandas, NumPy, SciPy, Scikit-learn, Seaborn, Matplotlib)
  • Why Anaconda?
  • Installation of Anaconda & other Python IDE
  • Python Objects, Numbers & Booleans, Strings, Container Objects, Mutability of Objects
  • Jupyter Notebook
  • Data Structures
  • Python Practical Session / Task

Numerical Python (NumPy)

  • Data Science and Python
  • What is NumPy?
  • NumPy Operations
  • Types of Arrays
  • Basic Operations
  • Indexing & Slicing
  • Shape Manipulation
  • Broadcasting
  • NumPy Practical Session / Task

Pandas Data Analysis

  • Why Pandas?
  • Pandas Features
  • Pandas File Read & Write Support
  • Data Structures
  • Understanding Series
  • Data Frame
  • Pandas Practical Session / Task Data Standardization
  • Missing Values
  • Data Operations
  • NumPy Practical Session / Task

Matplotlib & Seaborn Data Visualization

  • What is Data Visualization?
  • Benefits & Factors of Data Visualization
  • Data Visualization Considerations & Libraries
  • Data Visualization using Matplotlib
  • Advantages of Matplotlib
  • Data Visualization using Seaborn
  • What is a Plot and its types?
  • How to Plot with (x,y)?
  • How to Control Line Patterns and Colors
  • How to Implement Multiple Plots?
  • Matplotlib Practical Session / Task

Data Manipulation: Cleansing – Munging

  • Data Manipulation steps (Sorting, filtering, merging, appending, derived variables, etc)
  • Filling the missing values by using Lambda function and Skewness.
  • Cleansing Data with Python

Data Analysis: Visualization Using Python

  • Introduction exploratory data analysis
  • Important Packages for Exploratory Analysis (NumPy Arrays, Matplotlib, seaborn, Pandas, etc)
  • Univariate Analysis (Distribution of data & Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)
  • Creating Graphs- Bar/pie/line chart/histogram/ boxplot/ scatter/ density etc)
  • Descriptive statistics, Frequency Tables & summarization

Introduction to Artificial Intelligence (AI) & Machine Learning (ML)

  • What is Artificial Intelligence & Machine Learning?
  • What is Big Data?
  • Understanding the difference between Artificial Intelligence, Machine Learning & Deep Learning
  • Artificial Intelligence in Real World-Applications

Machine Learning Techniques & Algorithms

  • Types of Machine Learning
  • Machine Learning Algorithms
  • Hyper parameter optimization
  • Hierarchical Clustering
  • Implementation of Linear Regression
  • Performance Measurement
  • Principal component Analysis
  • How Supervised & Unsurprised Learning Model Works?
  • Machine Learning Project Life Cycle & Implementation
  • What is Scikit Learn, Regression Analysis, Linear Regression?
  • Difference between Regression & Classification
  • What is Logistic Regression and its implementation?
  • Best Machine Learning Approach

Decision Tree and Random Forest Algorithm

  • What is a Decision Tree and how it works?
  • What is Entropy, Information Gain, Decision Node?
  • In-depth study of Random Forest and understanding how it works?

Naive Bayes and KNN Algorithm

  • What is Naïve Bayes?
  • Advantages & Disadvantages of Naïve Bayes
  • why KNN?
  • Practical Implementation of Naïve Bayes
  • What is KNN and how does it work?
  • How do we choose K?
  • Practical Implementation of KNN Algorithm

Support Vector Machine Algorithm

  • What is Support Vector Machine (SVM)?
  • How Does SVM Work?
  • Applications of SVM
  • Why SVM?
  • Practical Implementation of SVM

Model Deployment & Tableau

  • Flask Introduction & Application
  • Django end to end
  • Working with Tableau
  • Data organisation
  • Creation of parameters
  • Advanced visualization
  • Dashboard data presentation

Introduction to Statistics

  • Descriptive Statistics
  • Sample vs Population Statistics
  • Random variables
  • Probability distribution functions
  • Expected value
  • Normal distribution
  • Gaussian distribution
  • Z-score
  • Central limit theorem
  • Spread and Dispersion
  • Hypothesis Testing
  • Z-stats vs T-stats
  • Type 1 & Type 2 error
  • Confidence Interval
  • ANOVA Test
  • Chi Square Test
  • T-test 1-Tail 2-Tail Test
  • Correlation and Co-variance

Introduction to Predictive Modelling

  • The concept of model in analytics and how to use it?
  • Different Phases of Predictive Modelling
  • Popular Modelling algorithms
  • Different kinds of Business problems - Mapping of Techniques
  • Common terminology used in Modelling & Analytics process

Data Exploration for Modelling

  • Visualize the data trends and patterns
  • Identify missing data & outliers’ data
  • EDA framework for exploring the data & identifying problems with the data by the help of pair plot.
  • What is the need for structured exploratory data?

Data Preparation

  • Merging
  • Normalizing the data
  • Feature Engineering
  • What is the need for Data preparation?
  • Aggregation/ Consolidation - Outlier treatment - Flat Liners - Missing Values-Dummy creation - Variable Reduction
  • Variable Reduction Techniques - Factor & PCA Analysis
  • Feature Selection
  • Feature scaling using Standard Scaler
  • Label encoding

Ensemble Learning Techniques

  • In-depth study of Ensemble Learning with Real Examples
  • How to Reduce Model Errors with Ensembles
  • Understanding Bias and Variance
  • Different Types of Ensemble Learning Methods
  • Feature Selection
  • Feature scaling using Standard Scaler
  • Label encoding

Web Scraping using Python Beautiful Soup

  • What is Web Scraping & Why Web Scraping?
  • Web Scraping using Beautiful Soup Practical Session / Task
  • Difference Between Web Scraping Software Vs. Web Browser
  • Web Scraping using Beautiful Soup Practical Session / Task
  • Web Scraping Considerations & Tools
  • Why Beautiful Soup?
  • Common Data & Page Formats on the Web
  • Practical Implementation of Web Scraping
  • Web Scraping Process
  • What is a Parser?
  • Importance of Parsing
  • What are the various Parsers?
  • How to Navigate the Parsers?
  • How to take Output – Printing & Formatting

Time Series Analysis

  • Why Time Series Analysis?
  • What is Time Series?
  • Time Series Components (Seasonality, Trend, Level & Cyclicity) and Decomposition
  • Classification of Techniques like Pattern based or Pattern less
  • Basic to Advance level Techniques (Averages, AR Models, Smoothening, ARIMA, etc)
  • Use Cases of Time Series Analysis
  • When Not to Use Time Series Analysis?
  • Understanding Forecasting Accuracy - MAPE, MAD, MSE, etc
  • Time Series Analysis Case Study - Practical Session / Task

Deep Learning

  • What is deep learning
  • The neuron
  • How do neural networks work?
  • Back propagation
  • ANN in Python
  • What are convolutional neural networks?
  • Installing Tensor Flow & Keras
  • CNN in Python
  • Activation function & Epoch

Natural Language Processing (NLP) & Text Mining

  • What is Natural Language Processing (NLP) & Why NLP?
  • NLP with Python
  • Sentiment analysis
  • Bags of words
  • Stemming
  • Tokenization
  • What is Text Mining?
  • Text Mining & NLP
  • Benefits, Components, Applications of NLP
  • NLP Terminologies & Major Libraries
  • NLP Approach for Text Data
  • What is Sentiment Analysis?
  • Steps for Sentiment Analysis
  • Sentiment Analysis Case Study - Practical Session / Task
  • Practical Implementation of NLP
  • NLP Case Study - Practical Session / Task

Market Basket Analysis

  • What is Market Basket Analysis & how it is used?
  • What is Association Rule Mining?
  • What is Support, Confidence & Lift
  • An Example of Association Rules
  • Market Basket Analysis Case Study - Practical Session / Task

Top Reasons to take Data Science Training ?

Data Science jobs are on the rise. The U.S. Bureau of Labor Statistics has acknowledged a massive growth in the Data Science industry. It has predicted a 28% increase in the number of Data Science jobs by 2026, which will generate around 11.5 million new job opportunities. So, the sky is the limit for skilled and qualified Data Science professionals.

Data Scientists get the most lucrative job offers since there is a skill shortage in the Data Science market. Companies hold no salary bar for certified and well-trained Data Scientists. For this reason, the average salary of Data Scientists is pretty high, ranging from $104,000 to $155,000 per annum.

Getting Data Science Training in Houston can expand your career paths. Once you acquire the best Data Science techniques, you can explore many careers like Data Analyst, Data Visualization Developer, Statistician, Data Scientist, BI Engineer, Database Administrator, Analytics Manager, Big Data Engineer, etc. 

SynergisticIT’s data science Training/Bootcamp graduates have been hired by leading companies such as Visa, Apple, PayPal, Walmart Labs, AutoZone, Wells Fargo, Capital One, Walgreens, Bank of America, SAP, Cisco Systems, Verizon, T-Mobile, Intuit, Ford, Hitachi, Western Union, Deloitte, Dell, USAA, Carfax, Humana, and many more. These employers offer competitive salaries ranging from $95,000 to $155,000, reflecting the high demand for well-rounded professionals.

Data Science Training Certification in Houston

Data Science is present everywhere, be it Retail, Manufacturing, Finance, IT, Entertainment, Healthcare, or Education, all industries are heaving relying on it. Thus, learning Data Science can give you access to work in any industry.

What sets SynergisticIT apart is its commitment to candidate success. The JOPP team doesn’t just train you—they connect you with employers, schedule interviews, and guide you every step of the way until you land your dream job. This level of support is rare in the bootcamp world and is a key reason why SynergisticIT’s placement rates are among the highest in the industry.

Enrich your Data Science knowledge today and get a chance to work in a Fortune 500 Company. Learn the latest best practices of Data Science from our leading industry experts. We are focused on accompanying you at each step of your professional endeavors. Our dedicated team has helped thousands of aspirants in securing a stable and high-paying Data Science jobs. Let’s help you too in achieving your career goals.

If you’re searching for the best online data science training / data analyst / data engineering / ML/AI bootcamp in Houston, Texas, look beyond traditional bootcamps. Choose a program that not only teaches data science in-depth but also ensures you get hired. With over 15 years of industry expertise, SynergisticIT’s Data Science Job Placement Program (JOPP) provides the most comprehensive training and career support available nationwide.

By enrolling, you gain access to advanced training, real-world projects, and staffing support that connects you directly with employers. For aspiring data scientists and ML engineers in Houston, this is the most reliable path to a high-paying, future-proof career.

Create a robust work portfolio to demonstrate your abilities in the field with the assistance of experienced mentors. Let’s help you achieve your career goals. SynergisticITHome of the Best Data Scientists and Software Programmers in the Bay Area!

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FAQ's on Data Science Training

What Our Candidates Say About Us ?

Google Reviewer

Being an international student in USA and realizing that I was on the verge of completing my CS degree with not enough experience or skills to crack the interviews I was desperate for some kind of breakthrough. I started looking for a tech Bootcamp which could work with my study schedule and yet offer me…

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Minh Ho

Good place for anyone struggling to find a technology job with bigger name clients. I worked with them for some time like a year back or so and after my experience with them I had upgraded my coding skills to the standards of major it organizations. Synergisticit is in my opinion one of the very…

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Menglee G.

Synergistic IT was the best decision I made for my career. During my time here, I worked on multiple projects and learned a lot of high demand skills for the competitive tech industry. They have amazing trainers who have lots of experience. I would recommend it to anyone who wants to become a professional in…

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