Data Science Training in Tucson

Best Data Science Training in Tucson

At SynergisticIT, we ensure you cope well with the fast-moving tech industry. Therefore, we provide industry-aligned Data Science training in Tucson that equips you with the latest market trends and in-demand techniques. Our Data Science training is suitable for all, whether you are a student, an intermediate learner, or a working professional with no technical background. We help our customers stand out in the competition by rewarding them with certifications. By the end of this Data Science training, you will be skilled in performing data analysis, building predictive models, automating tasks, and manipulating databases.

Why pursue a Data Science Career ?

Data has become an essential part of businesses today. Many companies hire skilled Data Scientists to analyze and interpret structured and unstructured data sets and mitigate potential risks. The vital importance of data analysis has surged the demand for Data Scientists. Let’s look at some benefits of pursuing a career in Data Science:

  • Plenty of career paths: Learning Data Science can open doors to different job options such as Data Scientist, Data Analyst, Machine Learning Engineer, Statistician, Big Data Engineer, BI Engineer, Analytics Manager, Data Visualization Developer, etc. You can also have better career prospects by attending Data Science training in Tucson.  

  • High-paying jobs: Getting Data Science training can reward you with lucrative job offers. As a Data Scientist, you can earn an average salary of $104,000 to $155,000 per annum based on your experience, location, and domain.

Best Data Science Training Program in Tucson
  • Entry to diverse sectors: Data Science enables you to work in the leading industries such as Education, Healthcare, Retail, Finance, IT, Transportation, and others. Thus, if you want to enlarge your work scope, you should consider getting upskilled in Data Science

  • Promising career: The Bureau of Labour Statistics (BLS) has forecasted a 28% increase in Data Science jobs by 2026. It will create around 11.8 million new jobs for Data Scientists. Leverage the opportunity to future-proof your career with Data Science training in Tucson.

  • Lower competition: Despite the rising demand for Data Scientists, the industry has a huge supply-demand. Companies struggle to find qualified Data Scientists, so by acquiring the necessary Data Science skills, you can head start a stable tech career without facing much competition.

Data Science Training Modules

Our Data Science training in Tucson has a comprehensive curriculum that focuses on building your analytical and computational skills. This training imparts knowledge of core and advanced Data Science concepts such as web scraping, NLP, Artificial Intelligence, predictive modeling, deep learning, data exploration, Machine Learning, data cleaning, data analysis, etc. The courseware of this training is strategically divided into different modules to help you learn Data Science from scratch. A major part of our training is based on practical exercises that enable you to gain hands-on experience.

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

Why choose SynergisticIT for Data Science Training in Tucson ?

There are thousands of Data Science bootcamps in Tucson, but SynergisticIT is a top choice for most learners. Below are some reasons to choose us over others:

Since our founding in 2010, SynergisticIT has been acknowledged as the most trusted online Data Science Bootcamp in Tucson.

We have a world-class faculty of Data Science professionals with 10+ years of field experience. They share profound knowledge and inculcate the best development practices in candidates.

When you enroll in our Data Science training in Tucson, you get end-to-end assistance from tech training, career coaching to job placement and onboarding.

We don’t impose any extra charges for repeating a Data Science training session.

SynergisticIT for Data Science Training in Tucson

This Data Science training works on your overall development. Our instructors can help you build solid problem-solving abilities and soft skills.

We have the highest success rate of 97.8%, with our candidates working at Fortune 500 Companies like TCS, IBM, Amazon, Cisco, PayPal, Apple, Google, etc. Most of our candidates get multiple job offers within 2 weeks of graduating with us.

We regularly test the knowledge and skills of our candidates on different levels through technical mock tests, psychometric tests, cognitive interviews to make them job-ready. Besides, our placement team provides additional tips on building a marketable resume, cover letter, and work portfolio.

All our candidates get rewarded with an industry-recognized certificate at the end of this Data Science training in Tucson. It gives them a competitive edge over non-certified candidates.

Data Science Training in Tucson

Who can take our Data Science Training ?

Anyone who wishes to make a mark in the Data Science industry can enroll in this Data Science training in Tucson. This training is best suited for:

Freshers.

College graduates/undergraduates.

Software developers/programmers.

Data Science aspirants.

Individuals wanting to improve their critical thinking abilities.

Professionals working on business intelligence, reporting tools, or data warehousing.

Start acquiring valuable Data Science and Data Analyst skills by training at the best online Data Science Bootcamp. 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!

train to grow- Machine Learning

Frequently Asked Questions on Data Science

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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