Data Science Training in Tacoma

Comprehensive Data Science Training in Tacoma

At SynergisticIt, our best-certified experts teach thorough Data Science courses. We will assist the applicants in our course in learning the fundamentals of data science over the period of five to six months. By acquiring the necessary technical skills and knowledge with our career-focused Data Science Training, you will stand out in the cutthroat data-driven business. Thanks to our task-based instruction, students will experience the technologies used during data science in the real world. Candidates will learn task automation, data analysis, the creation of prediction models, task automation, and data manipulation after finishing this comprehensive Data Science Training in Tacoma.

What are the eligibility criteria for joining this Data Science Training Bootcamp?

What are the eligibility criteria for joining this Data Science Training Bootcamp?

Attending this course is open to those who want to begin their careers in data science from scratch. You don't need to know any programming languages beforehand. Consequently, you are welcome to enroll in this Data Science Training Bootcamp in Tacoma as:

  • 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

How pursuing a career in Data Science lucrative?

Here are some significant reasons given below why pursuing a career in Data Science is lucrative:

Lucrative Pay: Since Data Science will optimize your earning potential, it should be your top choice for a job. Among the most lucrative technologies, data science pays between $104,000 and $155,000 annually, making it one of the highest-paying fields.

Work with Prominent Companies: According to tech organizations like Amazon, Apple, Microsoft, Google, IBM, Facebook, and others, data science is the most popular field right now. As a result, the Data Science Training program in Tacoma will help you to land a rewarding profession.

How pursuing a career in Data Science lucrative

Popular Tech Skill: The industry now requires 29% more skilled Data Scientists, while the number of candidates is only growing by 14%. It highlights the gap in the labor market between supply and demand. You can improve your Data Science skills, make up for any gaps in your knowledge, and boost your career prospects.

Opens the door to a lot of work chances: According to the Bureau of Labor Statistics, by 2026, there will be a 29% rise in the number of positions in the data science industry, which will result in more than 11.8 million new job opportunities for data science experts. The best investment, however, is Data Science Training in Tacoma, which can guarantee you a job for the foreseeable future.

What is the course curriculum of Data Science Training in Tacoma?

With a thorough course that covers the fundamentals of data science, as well as progressive ideas like web scraping, data analysis, model deployment, machine learning, data visualization, artificial intelligence, data structures, Python programming, predictive modeling, etc., Our online Data Science Training in Tacoma, is designed to meet the needs of students. In order to succeed in the cutthroat and ever-evolving Data Science sector, you will be guided by our professionals through the fundamental skills needed.

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 Tacoma?

With zillions of data produced each day, businesses have acknowledged the importance of collecting, organizing and interpreting data. It helps them understand customer behaviour and improve decision-making. Let’s check out if Data Science as a career is worth pursuing or not:

You will receive the best Data Science Training support at SynergisticIT from eminent experts who offer in-depth industry knowledge and more than 12+ years of expertise.

To help students understand concepts and knowledge better, we are regularly changing the curriculum of our Data Science Training program in Tacoma to reflect current market trends.

To further your understanding of Data Science, we provide theory lessons that are complemented by hands-on tasks.

Why choose SynergisticIT for Data Science Training in Tacoma?

You will experience the most dynamic learning environment at SyneristicIT because we hold our sessions in small groups so that we can get to know each student and help them develop for the cutthroat industry.

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

Our reliable relationships with top Fortune 500 companies enable us to secure employment for our tech-savvy individuals

We offer a widely recognized credential upon successfully completing our comprehensive Data Science Training in Tacoma.

What is the best Employment perspective after Data Science Training?

What is the best Employment perspective after Data Science Training?

AI-based technologies and Machine Learning have created significant career opportunities for Data Scientists as more companies use Data Science. Following your completion of our Data Science training in Tacoma, you may qualify for some of the highest-paying jobs listed below:

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

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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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