Data Science Training in Los Angeles

Best Online Data Science and AI Bootcamp in Los Angeles

SynergisticIT offers an immersive Online Data Science and AI training bootcamp in Los Angeles that empowers you with the right skills to launch a data-driven career. We assign the term Data Science Job Placement Program -JOPP- to our data science training/bootcamp as our focus in on getting our enrollees to get hired for Jobs

Los Angeles is not only the entertainment capital of the world but also a thriving hub for technology, healthcare, finance, and e-commerce. With the rapid adoption of artificial intelligence (AI) and machine learning (ML), the demand for skilled data scientists, data engineers, and analysts has skyrocketed. Companies such as Netflix, Disney, Hulu, Sony Pictures, Warner Bros., Snap Inc., TikTok, Amazon Studios, Google (LA offices), Riot Games, Ring, SpaceX, Northrop Grumman, UCLA Health, Cedars-Sinai, Activision Blizzard, Paramount, Tinder, TrueCar, DoorDash, and City of Los Angeles are actively hiring ML/AI professionals and data scientists to build intelligent systems, predictive models, and next-generation applications.

Why Data Science and Machine Learning Are Essential

Data science and ML/AI are transforming industries by enabling organizations to analyze massive datasets, uncover insights, and automate decision-making. These technologies have been evolving for decades, with data science as a discipline emerging in the early 2000s, built on foundations of statistics, mathematics, and computer science. Today, data science is future-proof because every industry—from healthcare to entertainment—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.

Reasons to pursue a Data Science Career ?

Before enrolling yourself in a Data Science training / Bootcamp in Los Angeles, check out some significant benefits of learning Data Science:

  • There is a global demand for Data Science professionals. The U.S. Bureau of Labour Statistics has projected a 28% increase in the number of Data Science jobs by 2026. It means that there will be around 11.8 million Data Science jobs in the U.S. alone. So, if you get competent in Data Science, you will have adequate employment opportunities.

  • Data Science is a lucrative career path that offers big paychecks. The average salary of Data Scientists ranges from $104,000 to $155,000, which makes them the highest-paid tech professionals.

  • Once you master the key aspects of Data Science, you can explore many career options such as Data Scientist, Data Analyst, Big Data Engineer, Statistician, Data Visualization Developer, BI Engineer, Analytics Manager, Database Administrator, etc.

Data Science Training Bootcamp in Los Angeles
  • A recent survey on Data Science has revealed a shortage of skilled resources in the job market. Reportedly, talent supply is scarce for Data Scientists as compared to its huge demand. One can leverage the opportunity to get upskilled through Data Science training in Los Angeles.

  • Data Science is omnipresent as every other industry is using it in some capacity. It widens the career prospects of skilled Data Scientists and gives them access to work in any sector from Healthcare, Manufacturing to Banking, IT, and Education.  

  • Employers in Los Angeles and beyond are asking for skills in:

    • Large Language Models (LLMs) and Generative AI
    • MLOps for productionizing ML models
    • AutoML for automating model selection and tuning
    • Deep Learning frameworks like TensorFlow and PyTorch
    • Natural Language Processing (NLP) for chatbots and sentiment analysis
    • Computer Vision for image and video analytics
    • Streaming Analytics with Apache Kafka and Spark

Course Content of our Data Science Training/Bootcamp

Our online Data Science training/ bootcamp in Los Angeles centers around the best practices of Data Science and analytics, including data manipulation, visualization, cleaning, exploration, preparation, mining, predictive modelling, web scraping, NLP, etc.

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 Naive 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 Certification Training in Los Angeles

Who Should Attend SynergisticIT's Data Science Training/Bootcamp ?

Anyone can consider this Data Science training to have better career possibilities. It is mainly intended for:

Individuals working on reporting tools, BI, and data warehousing

Professionals with a logistics, mathematical, or analytical background

Economists, Statisticians, and Mathematicians

Software programmers and Business analyst’s aspirants

Recent Grads who want to improve their Tech skills and critical thinking abilities and get hired

Jobseekers with career gaps or lacking real-world experience

Jobseekers who had layoffs due to Downsizing and want to get in demand tech stack

Data Science, Data Analytics, ML/AI Grads struggling to land interviews despite having tech skills.

Why Choose SynergisticIT for Data Science Training in Los Angeles ?

SynergisticIT is considered one of the best Data Science training Bootcamps in Los Angeles.

We have an experienced faculty with more than 15+ years of industry expertise.

Our dedicated instructors provide complete assistance until you get placed in a Fortune 500 Company.

Candidates can retake or repeat any class at no additional cost.

We have a higher success rate of 91%, with most of our candidates placed in renowned companies.

Our career-focused training acquaints candidates will all the necessary skills required for a successful Data Science career.

SynergisticIT’s JOPP is not just a bootcamp—it’s a remote, nationwide program that combines training with staffing services. Candidates are trained in-depth and then connected with employers. The program helps schedule interviews, provides mentorship, and handholds candidates until they are successfully hired. This is why it’s called a Job Placement Program and not just a coding bootcamp.

In fact, 30% of candidates who join SynergisticIT’s JOPP have already undertaken other coding bootcamps and failed to secure jobs. This highlights the importance of choosing a program that not only trains but also provides staffing support.

Data Science Training in Los Angeles

We prepare candidates for Data Science job interviews through technical mock tests, behavioural questions, and psychological assessments. It helps us evaluate your knowledge to ensure you’re job-ready.

SynergisticIT’s candidates 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.

SynergisticIT also offers the highest ROI compared to colleges and other bootcamps, ensuring that your investment in training pays off with real job outcomes. You can read more in their ROI blog which compares the program’s return on investment to traditional education paths.

End-to-End Career Support

What sets SynergisticIT apart is its commitment to candidate success. SynergisticIT 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.

Final Thoughts

If you’re searching for the best online data science training / data analyst / data engineering / ML/AI bootcamp in Los Angeles, 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.

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…

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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