Data Science Training Online in Phoenix

Phoenix, Arizona is one of the fastest‑growing technology and innovation hubs in the United States. With healthcare networks, aerospace leaders, fintech companies, and semiconductor giants investing billions in digital transformation, the demand for professionals skilled in data science, data analytics, data engineering, and machine learning/artificial intelligence (ML/AI) is skyrocketing. For jobseekers, the challenge is not simply learning a few tools—it’s about mastering a complete tech stack that employers in Phoenix expect. That’s why SynergisticIT’s Data Science Job Placement Program (JOPP) is the Best Data Science Training Bootcamp in Phoenix, Arizona , offering not just training but also staffing and job placement support.

In Phoenix, Arizona, demand for data scientists, data analysts, and AI engineers is strong, with opportunities across healthcare, finance, technology, and consulting.Companies actively hiring in the region include PayPal, TriWest Healthcare Alliance, Offerpad, Engage3, Affinaquest Technologies, Goodwill Industries International, ASM, Sustainable Talent, MSR Technology Group, Deloitte, Oscar Health, Republic Services, Molina Healthcare, MedImpact, Meritore, Banner Health, Honeywell Aerospace, American Express, Intel Corporation, Wells Fargo, JPMorgan Chase, USAA, CVS Health, and Arizona State University.

Overall, Phoenix offers competitive compensation, with most data and AI professionals earning $85,000 to $150,000 annually, making the city an attractive destination for long‑term career growth in data science and artificial intelligence.

Phoenix will continue to experience ongoing demand for data scientists, data analysts, and ML/AI engineers both now and in the future because the city is rapidly evolving into a major technology hub with strong investments across healthcare, finance, aerospace, and semiconductor industries. Organizations such as Banner Health, Molina Healthcare, American Express, Wells Fargo, JPMorgan Chase, Honeywell Aerospace, and Intel rely on advanced analytics and machine learning to improve patient outcomes, detect fraud, optimize risk models, and enhance engineering and manufacturing processes. With billions of dollars being invested by companies like Intel and TSMC in semiconductor facilities, Phoenix is creating thousands of jobs in AI, cloud computing, and data engineering.

Employers in Phoenix increasingly seek expertise in:

  • Data Science: Python, R, SQL, statistical modeling, and machine learning algorithms.
  • Data Analytics: Tableau, Power BI, SAS, and advanced Excel for visualization and reporting.
  • Data Engineering: Apache Spark, Hadoop, Kafka, ETL pipelines, Snowflake, and Databricks.
  • ML/AI: TensorFlow, PyTorch, Scikit‑Learn, NLP, computer vision, and reinforcement learning.
  • Cloud Platforms: AWS, Azure, and Google Cloud for scalable solutions.

Why Just Data Science Isn’t Enough

Many bootcamps focus narrowly on data science or ML/AI. But in Phoenix, employers want candidates who can:

  • Build and manage data pipelines (Data Engineering)
  • Analyze and visualize insights (Data Analytics)
  • Develop predictive models (Data Science)
  • Deploy AI solutions at scale (ML/AI)

Without this multi‑disciplinary skill set, jobseekers often struggle to secure roles. SynergisticIT’s program ensures candidates master all these domains together.

Why SynergisticIT Stands Apart

Not all bootcamps are equal. Many coding bootcamps in Phoenix provide surface‑level training and leave students to fend for themselves in the job market. SynergisticIT, however, has been in the tech industry for over 15 years, building a vast employer network and understanding exactly what companies want.

SynergisticIT’s Data Science Job Placement Program (JOPP) is not just a bootcamp—it’s a training + staffing solution. Candidates learn technologies in‑depth, build real projects, earn certifications, and receive direct job placement support.

SynergisticIT’s Data Science JOPP

Here’s why SynergisticIT’s JOPP is the best data science training Bootcamp in Phoenix, Arizona:

  • Comprehensive Curriculum: Covers data science, analytics, engineering, ML/AI, cloud, and DevOps.
  • Hands‑On Projects: Real‑world projects that mirror enterprise environments.
  • Certifications: Industry‑recognized credentials to boost resumes.
  • Interview Preparation: Resume building, mock interviews, and technical coaching.
  • Job Guarantee & Assistance: Active marketing of candidates, scheduling interviews, and ensuring job offers.
  • Remote Access: The program can be done online from anywhere in the USA, making it the online data science training Bootcamp in Phoenix, Arizona.

 

Why should you consider learning Data Science ?

Here are some significant reasons to learn Data Science:

  • A plethora of Employment Opportunities- The Bureau of Labour Statistics has projected exponential growth in the Data Science market. It has been predicted that there will be a 28% increase in the number of Data Science jobs by 2026, creating around 11.8 million new jobs. One can seize the opportunity to become competent in Data Science by enrolling in Data Science training in Phoenix.

  • High in-demand- Data Science is the most sought-after skill in technology, but there is an acute shortage of qualified data scientists. A recent report by Indeed reveals a 29% increase in the demand for Data Science applicants. However, the job seekers are growing at a much slower pace of 14%. It highlights a gap between supply and demand. You can bridge this gap by getting upskilled in Data Science.

Data Science Training Bootcamp in Phoenix
  • Work across different sectors- The biggest advantage of starting a Data Science career is that it allows you to work across various areas and industries. Since there is a pressing need for Data Science professionals in Healthcare, Banking, Automotive, IT, Manufacturing, Telecommunications, and other leading sectors, you can have good odds to work in diverse fields

  • Futureproof career- Data has become the driving force for all businesses in 21st Century. So, if you’re planning to develop your Data Science knowledge, you are placing yourself in a strong position for a stable career in the future.

  • In Phoenix, Arizona, salaries for data scientists, data analysts, and AI engineers vary by role and level, reflecting the city’s growing demand for advanced tech talent across industries like healthcare, finance, aerospace, and e‑commerce. Entry‑level data analysts at companies such as Republic Services, Goodwill Industries, and TriWest Healthcare Alliance typically earn between $70,000 and $85,000 annually, while mid‑level analysts and data scientists at firms like PayPal, Oscar Health, and Molina Healthcare command salaries in the range of $90,000 to $115,000. Senior data scientists and AI engineers working at enterprise employers such as Intel, Honeywell Aerospace, American Express, Wells Fargo, and JPMorgan Chase often earn between $125,000 and $155,000, with specialized AI roles in machine learning and deep learning reaching $160,000+.

  • More Career Options- After getting Data Science training in Phoenix, you can explore multiple job options such as Data Scientists, BI Specialists, Data Engineers, Big Data Engineers, Data Architect, Data Visualization Developer, Business Analytics Specialists, BI Solutions Architect, Statistician, Analytic Manager, etc.

Why Choose JOPP Over Other Bootcamps

Instead of spending money on multiple bootcamps or cheaper programs that fail to deliver, jobseekers can enroll in SynergisticIT’s JOPP. The program covers all technologies employers demand—data engineering, analytics, ML/AI, and data science—plus projects, interview prep, and certifications.

This holistic approach ensures candidates are job‑ready and not left struggling after graduation.

Tech Stack in JOPP

  • Programming: Python, R, SQL
  • Data Science: Pandas, NumPy, Scikit‑Learn, TensorFlow, PyTorch
  • Data Engineering: Hadoop, Spark, Kafka, Snowflake, Databricks
  • Data Analytics: Tableau, Power BI, SAS, Excel
  • Cloud & DevOps: AWS, Azure

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
Data Science Training in Phoenix

Who Should Attend Data Science Training ?

Anyone can sign up for our Data Science training in Phoenix to have better career prospects. We have mainly curated this for:

Fresher who wants to build some analytical skills to start a Data Science career

Professionals with an analytical, logistics, or mathematical background

Software developers or programmers

Aspiring Business Analyst and Data Scientists

Individuals working on Business intelligence, data warehousing, and reporting tools

Benefits of taking Data Science Training from SynergisticIT

We provide the best Data Science Training in Phoenix.

Our instructors have 10+ years of experience in Data Science.

We offer lifetime access to our updated course material.

Most bootcamps only train students. SynergisticIT’s JOPP goes further:

  • Markets candidates to employers
  • Connects and schedules interviews
  • Provides ongoing support until candidates are hired

This training + staffing model is why it’s called a Job Placement Program and not just a bootcamp.

Candidates can retake any class at no extra charges.

You learn by doing and applying Data Science techniques on real-life projects.

We prepare our candidates for technical interviews, behavioural questions, psychological assessments, etc.

Data Science Certification Training in Phoenix

SynergisticIT’s alumni are hired by top companies at salaries ranging from $95,000 to $155,000. Employers include:

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.

There may be hundreds of data science Bootcamps in Phoenix, Arizona, but if your goal is to get hired after completing the program, there is only one choice: SynergisticIT’s Best Data Science Training Bootcamp in Phoenix, Arizona.

With its proven track record, industry‑aligned curriculum, and staffing support, SynergisticIT ensures candidates don’t just learn—they succeed. For jobseekers, SynergisticIT’s program is the sure‑shot way to secure employment in Phoenix’s competitive data science market.

Begin your tech career journey with the best data science training Bootcamp in Phoenix, Arizona with job assistance and job guarantee.

SynergisticITHome of the Best Data Scientists and Software Programmers!

train to grow- Machine Learning

FAQs 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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