Data Science Training Bootcamp Online in Columbus

Best Data Science Bootcamp in Columbus, Ohio: Job-Oriented Data Science Training with SynergisticIT

SynergisticIT’s Best Data Science Training Bootcamp in Columbus, Ohio: The Job Placement Program (JOPP) Advantage.

SynergisticIT’s Data Science Job Placement Program (JOPP) is not just another bootcamp—it’s a comprehensive, results-driven pathway designed to get jobseekers hired with top tech clients at high salaries. With over 15 years in the tech industry, SynergisticIT has helped more than 10,000 candidates launch successful careers, boasting a 91.5% placement rate and average starting salaries between $95,000 and $155,000.

Key Differentiators:

  • Industry-Aligned Curriculum: SynergisticIT’s curriculum is continuously updated based on direct feedback from tech clients and participation in major industry events like Oracle CloudWorld, Gartner Data Analytics Summit, and OCW.
  • Multi-Stack Training: The program covers data engineering, analytics, ML/AI, data science, business intelligence, cloud platforms, DevOps, and MLOps—ensuring graduates are equipped for the full spectrum of data roles.
  • Hands-On Projects: Candidates work on enterprise-level projects, including customer churn prediction, recommendation systems, fraud detection, NLP-powered chatbots, and computer vision models.
  • Certification Preparation: Training includes preparation for industry-recognized certifications (Power BI, Tableau, Snowflake, Databricks, Azure, AWS, Java, DevOps) at no extra cost.
  • Interview and Resume Support: Access to a database of 5,000+ interview questions, mock interviews, resume optimization, and personalized mentoring.
  • Active Marketing and Placement: SynergisticIT markets candidates directly to a network of 24,000+ tech clients, schedules interviews, and provides handholding until a job offer is secured.
  • Transparent Outcomes: Offer letters and placement data are verifiable, with graduates landing roles at 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, and Humana.

Unlike many bootcamps that rely on flashy ads and vague “job guarantee” language, SynergisticIT positions itself around real results and industry exposure. The program’s superior effectiveness is demonstrated by the fact that 30% of JOPP candidates previously attended other bootcamps without success, only to achieve successful placements after enrolling in SynergisticIT.

Columbus has become one of the Midwest’s strongest data and analytics hubs, with major employers across finance, insurance, retail, healthcare, research, and technology actively hiring data scientists. Companies hiring data scientists include Deloitte, State of Ohio – Medicaid, Vertiv, The Hartford, Nationwide, CVS Health, Mettler Toledo, JPMorgan Chase, Ocean Blue Solutions, American Electric Power (AEP), Abercrombie & Fitch, Battelle, Huntington National Bank, CoverMyMeds, McKesson, Defense Logistics Agency, TrueCommerce, Ohio State University, Gradient AI, Webflow, Dropbox, Coinbase, Atticus, Path Robotics, and CapTech. These companies represent a mix of enterprise corporations, financial institutions, healthcare organizations, retail giants, and advanced research labs, all of which rely heavily on data-driven decision-making.

Data scientist salaries in Columbus vary depending on experience, industry, and specialization. Entry-level and mid-level roles typically fall between $93,000 and $138,000, as seen in listings from companies like Deloitte and The Hartford. Senior-level roles, such as those at American Electric Power (AEP), offer salaries ranging from $96,000 to $125,000. Highly specialized or federal positions, such as those with the U.S. Department of Defense, can reach $106,365 per year or higher. Broader market data shows that many Columbus-based data scientist roles fall within $95,000 to $160,000, especially for advanced machine learning, AI, or risk analytics positions. These ranges reflect Columbus’s growing demand for analytics talent and the city’s competitive compensation landscape.

Columbus is home to a rapidly expanding tech ecosystem supported by major industries such as finance, insurance, retail, logistics, and healthcare. Companies like Nationwide, JPMorgan Chase, Huntington Bank, and CoverMyMeds rely heavily on predictive analytics, machine learning, and AI to optimize operations, detect fraud, personalize customer experiences, and improve healthcare outcomes. The region’s strong university presence, including Ohio State University, fuels innovation and research, further increasing the need for data professionals. As organizations continue adopting cloud platforms, AI-driven automation, and advanced analytics, the demand for data scientists will only grow. Columbus’s cost-effective business environment also attracts companies relocating or expanding their analytics teams, ensuring long-term job stability for data science professionals.

Learning data science and analytics in Columbus is not just a career move—it’s a strategic investment in future-proofing your professional journey. The region’s data center boom, driven by the proliferation of AI and cloud computing, has positioned Columbus as a nationally recognized hub for data infrastructure and innovation. With over 200 data centers—more than half located in Columbus—local companies are leveraging data to optimize operations, enhance customer experiences, and drive competitive advantage.

For jobseekers, the implications are clear: mastering data science and analytics opens doors to high-paying roles, rapid career advancement, and the opportunity to shape the future of business and technology in Ohio. Entry-level data scientists in Columbus can expect salaries starting at $82,000, with senior positions reaching up to $315,000 at top firms. The city’s tech workforce is projected to grow twice as fast as the national average, translating to approximately 350,000 new tech jobs annually across the U.S. and a robust pipeline of opportunities in Columbus.

While data science and ML/AI training are foundational, Columbus employers increasingly demand multi-stack expertise that spans data engineering, analytics, and business intelligence. The modern data professional must be proficient in:

  • Data Engineering: Building and maintaining data pipelines, ETL processes, and scalable infrastructure.
  • Data Analytics: Interpreting data, creating dashboards, and communicating insights to stakeholders.
  • Cloud Platforms: Deploying and managing solutions on AWS, Azure, or Google Cloud.
  • Business Intelligence (BI): Using tools like Tableau and Power BI for data visualization and reporting.
  • DevOps and MLOps: Automating deployment, monitoring, and maintenance of data and ML solutions.

This holistic skill set is essential for jobseekers aiming to secure high-paying roles and long-term career growth. Employers value candidates who can bridge the gap between raw data and actionable insights, ensuring that data-driven strategies translate into measurable business outcomes.

Not All Bootcamps Are Equal: The Importance of In-Depth Learning and Industry Experience

The proliferation of coding bootcamps has led to significant variability in quality, outcomes, and long-term career value. Many bootcamps offer compressed timelines, limited curriculum scope, and superficial learning experiences that fail to prepare candidates for the complexities of modern data roles. Common pitfalls include:

  • High Tuition Costs: Bootcamps often charge $10,000–$20,000, rivaling traditional degrees but without federal aid or accreditation.
  • Shallow Coverage: Short formats prioritize speed over mastery, resulting in limited retention and career flexibility.
  • Misleading Job Guarantees: Placement statistics are frequently inflated, with graduates compelled to pay fees even for low-paying jobs.
  • Minimal Marketing Support: Candidates are left to fend for themselves after training, struggling to secure interviews and offers.

SynergisticIT addresses these challenges by offering:

  • Comprehensive, Multi-Stack Coverage: Training goes beyond basics to include advanced concepts, real-world projects, and certifications.
  • Industry Interaction: Regular participation in tech events ensures curriculum relevance and alignment with employer needs.
  • Transparent Payment Model: Only partial fees are taken upfront, with the balance payable after securing a job of $81,000 or higher.
  • Verified Outcomes: Placement rates, salary data, and offer letters are publicly available for scrutiny.

For jobseekers in Columbus, choosing a bootcamp with deep industry experience and proven results is essential for maximizing ROI and career success.

 

Why should you take SynergisticIT’s Best Data Science Training Bootcamp ?

In the heart of Ohio, Columbus stands as a beacon for technological innovation, economic growth, and data-driven transformation. The city’s tech sector has surged by 22% in job opportunities over the past year, making it the state’s leading destination for aspiring data professionals. Major employers such as JPMorgan Chase, Accenture, and Nationwide Insurance have established robust operations in Columbus, fueling demand for skilled talent in data science, analytics, engineering, and machine learning/artificial intelligence (ML/AI). This dynamic environment is further amplified by the city’s affordable living costs, vibrant startup ecosystem, and strategic investments in R&D and tech incubators.

  • Data Science jobs are tremendously growing on LinkedIn. By the year 2026, it is expected to have 11.5 million new jobs. This makes Data Science a highly employable industry where one can never run out of job opportunities.

  • Data Scientists are the highest-paid workers in the IT sector. An entry-level Data Scientist gets an average salary of $104,000 per annum, which can go as high as $155,000 after some experience. 

  • Different industries like Finance, Transportation, IT, Manufacturing, Healthcare, and others use Data Science technology. Thus, if you take Data Science training in Columbus, you will be at good odds to enter diverse sectors. 

Data Science Certification Training in Columbus
  • Getting Data Science training can enlarge your career options. After acquiring Data Science competency, you can explore various job positions like Data Scientist, Big Data Engineer, BI Specialist, Data Visualization Developer, Business Analytics Specialist, Analytic Manager, etc.

  • Despite being high in demand, there remains an acute shortage of skilled and qualified Data Scientists. One can leverage the gap between supply and demand of Data Science professionals by getting upskilled through Data Science training in Columbus.

Course Content of our Best Data Science Bootcamp

Data Analytics & BI:

  • Power BI, Tableau, SAS, SQL, Data Cleaning & Transformation (ETL)

Data Engineering:

  • Apache Spark, Databricks, Snowflake, Hadoop Ecosystem, Kafka, AWS S3/Glue, GCP BigQuery/Dataflow, Azure Data Lake, ETL & Data Pipelines

Data Science & Statistics:

  • Python Libraries (NumPy, Pandas, SciPy, Matplotlib, Seaborn), EDA, Statistical Methods, Bayesian Inference, Time Series Analysis, Regression Models, Clustering & Dimensionality Reduction

Machine Learning & AI:

  • Programming (Python, R, Jupyter), Data Handling (NumPy, Pandas), Visualization (Matplotlib, Seaborn, Plotly), Supervised/Unsupervised Learning, Ensemble Methods, Deep Learning (DNNs, CNNs, RNNs, Autoencoders), Libraries (Scikit-learn, TensorFlow, PyTorch, Keras), NLP (Text Preprocessing, Sentiment Analysis, NER, Transformers), LLMs & GenAI (Hugging Face, GPT-based Models), Model Optimization, Cloud AI Tools (AWS SageMaker, Azure ML, GCP Vertex AI), AI Ethics & Responsible AI

 

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 Bootcamp in Columbus

Who can take our Data Science Training ?

Anyone who wants to acquire advanced data analytics skills can take this Data Science training in Columbus. It is designed for both professionals as well as freshers with no technical background. So, you can enroll, whether you are a:

College Graduate/Undergraduate

Software Developer/Programmer

Data Science Aspirant

Professionals working on Business Intelligence, data warehousing, or reporting tools

Beginners wanting to build their analytical skills

Reasons to join the Best Data Science Training Bootcamp in Columbus, Ohio

Program Outcomes: High Salaries, Employer Examples, and Comprehensive Course Coverage

SynergisticIT’s Data Science JOPP delivers outcomes that consistently exceed those of other bootcamps and training companies:

  • Salary Range: Graduates routinely earn $95,000–$155,000, with some exceeding $150,000 in their first role.
  • Employer Network: Placement with Fortune 500 companies and tech leaders, including 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, and Humana.
  • Comprehensive Coverage: The program includes data engineering, analytics, ML/AI, data science, business intelligence, cloud platforms, DevOps, MLOps, hands-on projects, interview prep, and certifications.
  • Online Availability: JOPP is fully remote and accessible nationwide, allowing candidates in Columbus and across the USA to participate without relocation.
  • Active Placement Process: SynergisticIT markets candidates, schedules interviews, and provides support until a job offer is secured.
  • Post-Job Support: Continuous support for 12 months after placement, ensuring long-term career success.

Many jobseekers waste time and money on 4–5 bootcamps or cheaper training companies that fail to deliver results. Common issues include:

  • Superficial Learning: Compressed timelines and limited curriculum lead to shallow understanding and poor job readiness.
  • Misleading Guarantees: Candidates are forced to pay fees even for low-paying jobs, with little support for career advancement.
  • Minimal Marketing: Lack of direct client marketing leaves candidates struggling to secure interviews.
  • Inconsistent Outcomes: Placement rates and salary data are often inflated or unverifiable.

SynergisticIT’s JOPP eliminates these risks by offering:

  • Comprehensive, Multi-Stack Training: Covering all essential domains and technologies.
  • Verified Outcomes: Transparent placement rates, salary data, and offer letters.
  • Active Marketing and Placement: Direct client marketing and interview scheduling until hire.
  • Flexible Payment Model: Partial fees upfront, balance payable only after securing a job of $81,000 or higher.

For jobseekers in Columbus, choosing SynergisticIT’s JOPP is the surest path to high-paying, future-proof careers in data science and analytics.

Our Data Science faculty updates the training curriculum regularly, so you get lifetime access to industry-relevant course material.

We provide career coaching and guidance on overcoming challenges in securing a Data Science job. Our team takes mock tests and cognitive interviews to ensure you are job-ready.

Our solid association with the top Fortune 500 Companies allows us to bag a position for our technically trained candidates.

Data Science Training in Columbus

While learning Data Science at SynergisticIT, you will get online theory sessions along with practical exercises. It helps you to deeply understand all the topics covered in the class. 

SynergisticIT’s transparent payment model is designed to minimize financial risk for candidates:

  • Partial Fees Upfront: Candidates pay approximately $10,000 upfront to ensure commitment and access to quality upskilling and marketing.
  • Balance Payable After Hire: The remaining $26,000 is payable in installments over 24 months only after securing a job of $81,000 or higher.
  • No Payment for Low-Paying Jobs: Payments do not start until a qualifying job offer is obtained, ensuring alignment with candidate success.
  • Refund Policy: In the rare event that a candidate does not secure a job offer, fees are refunded (subject to attendance, certification, and assessment requirements).

This model stands in stark contrast to other bootcamps that require full payment upfront or force candidates to pay even for low-paying jobs.

SynergisticIT’s focus is on delivering real, measurable outcomes—not hype or superficial guarantees. The program’s superior effectiveness is demonstrated by:

  • 91.5% Placement Rate: Verified by offer letters and placement data.
  • High Salaries: Graduates routinely earn $95,000–$155,000, with some exceeding $150,000 in their first role.
  • Top Employers: Placement with Fortune 500 companies and tech leaders.
  • Comprehensive Support: Training, marketing, interview prep, and post-job support.

Unlike flashy bootcamps that rely on marketing language and inflated statistics, SynergisticIT delivers results that are transparent, verifiable, and aligned with candidate success.

SynergisticIT’s Data Science JOPP is fully remote and accessible nationwide, allowing candidates in Columbus and across the USA to participate without relocation. The program features:

  • Live, Instructor-Led Sessions: 5–7 hours daily for 5–7 months, with unlimited access until job readiness.
  • Small Batch Sizes: Personalized attention and support.
  • Flexible Scheduling: Accommodates working professionals and career changers.
  • Post-Job Support: Continuous support for 12 months after placement.

This online, nationwide model ensures that candidates can access high-quality training and placement support regardless of location.

Start Your Tech Career Journey with SynergisticIT

If you’re a jobseeker in Columbus, Ohio, looking for the best data science training bootcamp with guaranteed job placement, SynergisticIT’s Data Science JOPP is your sure-shot path to success. With comprehensive multi-stack training, industry-aligned curriculum, active marketing, and a transparent payment model, SynergisticIT delivers outcomes that consistently exceed those of other bootcamps and training companies.

Ready to launch your tech career?

Don’t settle for superficial training or empty promises. Choose SynergisticIT—the best data science bootcamp in Columbus, Ohio—for real results, high salaries, and a future-proof career in data science, analytics, engineering, and AI.

SynergisticIT—The Best Choice for Data Science Training and Job Placement in Columbus, Ohio

 

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What Our Candidates Say About Us ?

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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…

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…

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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