Data Science Training in Spokane

Best Data Science Training in Spokane

At SynergisticIt, we provide comprehensive Data Science training taught by the best certified experts. In our course, we will guide the candidates to learn the core concepts of Data Science from the beginning within 5-6 months. Through our career-focused Data Science training, you will be prominent in the competitive data-driven industry by developing the required technical skills and knowledge. With our task-based training, students will get real-world exposure to the technologies used in Data Science. After completing this one of the best Data Science Training in Spokane, candidates will become skilled in task automation, Data analysis, predictive model development, task automation, and data manipulation.

Eligibility to join this Data Science Training Bootcamp in Spokane

Eligibility to join this Data Science Training Bootcamp?

Candidates who want to start their career in Data Science from the beginning are eligible to attend this training. It needs no previous coding experience or knowledge. Thus, you can join this Data Science training Bootcamp in Spokane as a:

  • College Graduate
  • Fresher
  • Software Programmer
  • Statistician, Economist, and Mathematician
  • Professionals with a logistics or analytical background
  • People working on reporting tools, business intelligence, and data warehousing

Why pursue a career in Data Science?

  • Lucrative Wages: You must consider Data Science as the best career option because it will maximize your earning potential. Data Science is one of the most lucrative technologies that provide you with the highest wages, like $104,000-$155,000 per annum.

  • Work with Prominent Firms: Data Science turns up as the most trending tech giants like Amazon, Apple, Microsoft, Google, IBM, Facebook, and others. Therefore, attending Data Science training in Spokane enables you to find a beneficial job.

  • Popular Tech Skill: The need in the market for skilled Data Scientists is increased by 29%, comparing the growth of the Data Scientist applicants is still 14%. It indicates the supply-demanded void in the job market. You can get upskilled in Data Science, fill the skill deficit, and increase your professional value.

Why pursue a career in Data Science
  • Opens the door of many job opportunities: As per the Bureau of Labor Statistics record, by 2026, the jobs in the Data Science sector will increase up to 29%, and it will open the door to more than 11.8 million new job opportunities for the position as a Data Science professionals. However, choosing Data Science training in Spokane is the best investment to have a secure job in your future. 

Topics of our Online Data Science Training in Spokane

Our Online Data Science training in Spokane has a comprehensive course that covers the fundamentals of Data Science and top-level concepts like Web Scraping, Data Analysis, Model Deployment, Machine Learning, Data Visualization, Artificial Intelligence, Data Structures, Python programming, Predictive Modeling, etc. Our experts will guide you through the essential skills required to blossom in the competitive and fast-moving Data Science industry. 

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 Spokane?

At SynergisticIT, you will get the best Data Science Training assistance from prominent professionals who provide extensive industry knowledge with more than 12+ years of experience.

We are constantly updating the curriculum of our Data Science Training timely with the modern market tendencies to provide a better understanding of concepts and knowledge.

We offer theory sessions which are supplemented with practical exercises to exacerbate your knowledge of Data Science.

At SyneristicIT, you will get the most interactive environment because we take classes in small groups to see every individual and help them grow for the competitive sector.

Why choose SynergisticIT for Data Science Training in Spokane

We prepare our students for tech job interviews through mock tests, cognitive interviews, psychometric tests, and soft skills training.

Our stable connection with leading Fortune 500 Organizations allows us to bag a job position for our tech-prepared candidates.

After completing Data Science training in Spokane, we provide an industry-acclaimed certificate.

Best Career Opportunities after Data Science Training

Best Career Opportunities after Data Science Training

As more industries harness Data Science, AI-based solutions, and Machine Learning have formed excellent growth prospects for Data Science professionals. Below are some of the top-paying job possibilities you can assume after completing our Data Science training in Spokane:

  • Data Scientist ($120,103 per annum)
  • BI Engineer ($117,044 per annum)
  • Big Data Engineer ($103,092 per annum)
  • Analytics Manager ($112,467 per annum)
  • Data Visualization Developer ($105,501 per annum)
  • Business Analytics Specialist ($84,601 per annum)
  • Data Engineer ($125,732 per annum)
  • BI Solutions Architect ($120,539 per annum)
  • BI Specialist ($90,286 per annum)
  • Statistician ($97,643 per annum)

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. SynergisticIT- Home 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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