CERTIFIED DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYST COURSE FEES IN SEATTLE

Live Virtual

Instructor Led Live Online

2,060
1,167

  • IABAC® Certification
  • 6-Month | 200+ Learning Hours
  • 20 HOURS LEARNING A WEEK
  • 10 Capstone & 1 Client Project
  • 365 Days Flexi Pass + Cloud Lab
  • Internship + Job Assistance

Blended Learning

Self Learning + Live Mentoring

1,030
665

  • Self Learning + Live Mentoring
  • IABAC® Certification
  • 1 Year Access To Elearning
  • 10 Capstone & 1 Client Project
  • Job Assistance
  • 24*7 Learner assistance and support

Corporate Training

Customize Your Training


  • Instructor-Led & Self-Paced training
  • Customized Learning Options
  • Industry Expert Trainers
  • Case Study Approach
  • Enterprise Grade Learning
  • 24*7 Cloud Lab

ARE YOU LOOKING TO UPSKILL YOUR TEAM ?

Enquire Now

UPCOMING DATA ANALYST ONLINE CLASSES IN SEATTLE

BEST DATA ANALYTICS CERTIFICATIONS

The entire training includes real-world projects and highly valuable case studies.

IABAC® certification provides global recognition of the relevant skills, thereby opening opportunities across the world.

images not display images not display

WHY DATAMITES INSTITUTE FOR DATA ANALYST COURSE

Why DataMites Infographic

SYLLABUS OF DATA ANALYST CERTIFICATION IN SEATTLE

MODULE 1: DATA ANALYSIS FOUNDATION

• Data Analysis Introduction
• Data Preparation for Analysis
• Common Data Problems
• Various Tools for Data Analysis
• Evolution of Analytics domain

MODULE 2: CLASSIFICATION OF ANALYTICS

• Four types of the Analytics
• Descriptive Analytics
• Diagnostics Analytics
• Predictive Analytics
• Prescriptive Analytics
• Human Input in Various type of Analytics

MODULE 3: CRIP-DM Model

• Introduction to CRIP-DM Model
• Business Understanding
• Data Understanding
• Data Preparation
Modeling, Evaluation, Deploying,Monitoring

MODULE 4: UNIVARIATE DATA ANALYSIS

• Summary statistics -Determines the value’s center and spread.
• Measure of Central Tendencies: Mean, Median and Mode
• Measures of Variability: Range, Interquartile range, Variance and Standard Deviation
• Frequency table -This shows how frequently various values occur.
• Charts -A visual representation of the distribution of values.

MODULE 5: DATA ANALYSIS WITH VISUAL CHARTS

• Line Chart
• Column/Bar Chart
• Waterfall Chart
• Tree Map Chart
• Box Plot

MODULE 6: BI-VARIATE DATA ANALYSIS

• Scatter Plots
• Regression Analysis
• Correlation Coefficients

MODULE 1: PYTHON BASICS

• Introduction of python
• Installation of Python and IDE
• Python Variables
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators

MODULE 2: PYTHON CONTROL STATEMENTS

• IF Conditional statement
• IF-ELSE
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements

MODULE 3: PYTHON DATA STRUCTURES

• Basic data structure in python
• Basics of List
• List: Object, methods
• Tuple: Object, methods
• Sets: Object, methods
• Dictionary: Object, methods

MODULE 4: PYTHON FUNCTIONS

• Functions basics
• Function Parameter passing
• Lambda functions
• Map, reduce, filter functions

MODULE 1 : OVERVIEW OF STATISTICS 

  • Introduction to Statistics
  • Descriptive And Inferential Statistics
  • Basic Terms Of Statistics
  • Types Of Data

MODULE 2 : HARNESSING DATA 

  • Random Sampling
  • Sampling With Replacement And Without Replacement
  • Cochran's Minimum Sample Size
  • Types of Sampling
  • Simple Random Sampling
  • Stratified Random Sampling
  • Cluster Random Sampling
  • Systematic Random Sampling
  • Multi stage Sampling
  • Sampling Error
  • Methods Of Collecting Data

MODULE 3 : EXPLORATORY DATA ANALYSIS 

  • Exploratory Data Analysis Introduction
  • Measures Of Central Tendencies: Mean, Median And Mode
  • Measures Of Central Tendencies: Range, Variance And Standard Deviation
  • Data Distribution Plot: Histogram
  • Normal Distribution & Properties
  • Z Value / Standard Value
  • Empherical Rule  and Outliers
  • Central Limit Theorem
  • Normality Testing
  • Skewness & Kurtosis
  • Measures Of Distance: Euclidean, Manhattan And MinkowskiDistance
  • Covariance & Correlation

MODULE 4 : HYPOTHESIS TESTING 

  • Hypothesis Testing Introduction
  • P- Value, Critical Region
  • Types of Hypothesis Testing
  • Hypothesis Testing Errors : Type I And Type Ii
  • Two Sample Independent T-test
  • Two Sample Relation T-test
  • One Way Anova Test
  • Application of Hypothesis testing

MODULE 1: COMPARISION AND CORRELATION ANALYSIS

• Data comparison Introduction,
• Performing Comparison Analysis on Data
• Concept of Correlation
• Calculating Correlation with Excel
• Comparison vs Correlation
• Hands-on case study : Comparison Analysis
• Hands-on case study Correlation Analysis

MODULE 2: VARIANCE AND FREQUENCY ANALYSIS

• Variance Analysis Introduction
• Data Preparation for Variance Analysis
• Performing Variance and Frequency Analysis
• Business use cases for Variance Analysis
• Business use cases for Frequency Analysis

MODULE 3: RANKING ANALYSIS

• Introduction to Ranking Analysis
• Data Preparation for Ranking Analysis
• Performing Ranking Analysis with Excel
• Insights for Ranking Analysis
• Hands-on Case Study: Ranking Analysis

MODULE 4: BREAK EVEN ANALYSIS

• Concept of Breakeven Analysis
• Make or Buy Decision with Break Even
• Preparing Data for Breakeven Analysis
• Hands-on Case Study: Manufacturing

MODULE 5: PARETO (80/20 RULE) ANALSYSIS

• Pareto rule Introduction
• Preparation Data for Pareto Analysis,
• Performing Pareto Analysis on Data
• Insights on Optimizing Operations with Pareto Analysis
• Hands-on case study: Pareto Analysis

MODULE 6: Time Series and Trend Analysis

• Introduction to Time Series Data
• Preparing data for Time Series Analysis
• Types of Trends
• Trend Analysis of the Data with Excel
• Insights from Trend Analysis

MODULE 7: DATA ANALYSIS BUSINESS REPORTING

• Management Information System Introduction
• Various Data Reporting formats
• Creating Data Analysis reports as per the requirements

MODULE 1: DATA ANALYTICS FOUNDATION

• Business Analytics Overview
• Application of Business Analytics
• Benefits of Business Analytics
• Challenges
• Data Sources
• Data Reliability and Validity

MODULE 2: OPTIMIZATION MODELS

• Predictive Analytics with Low Uncertainty;Case Study
• Mathematical Modeling and Decision Modeling
• Product Pricing with Prescriptive Modeling
• Assignment 1 : KERC Inc, Optimum Manufacturing Quantity

MODULE 3: PREDICTIVE ANALYTICS WITH REGRESSION

• Mathematics behind Linear Regression
• Case Study : Sales Promotion Decision with Regression Analysis
• Hands on Regression Modeling in Excel

MODULE 4: DECISION MODELING

• Predictive Analytics with High Uncertainty
• Case Study-Monte Carlo Simulation
• Comparing Decisions in Uncertain Settings
• Trees for Decision Modeling
• Case Study : Supplier Decision Modeling - Kickathlon Sports Retailer

MODULE 1: MACHINE LEARNING INTRODUCTION

• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised

MODULE 2: ML ALGO: LINEAR REGRESSSION

• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Hands-on Linear Regression with ML Tool

MODULE 3: ML ALGO: LOGISTIC REGRESSION

• Introduction to Logistic Regression;
• Classification & Sigmoid Curve
• Hands-on Logistics Regression with ML Tool

MODULE 4: ML ALGO: KNN

• Introduction to KNN; Nearest Neighbor
• Regression with KNN
• Hands-on: KNN with ML Tool

MODULE 5: ML ALGO: K MEANS CLUSTERING

• Understanding Clustering (Unsupervised)
• Introduction to KMeans and How it works
• Hands-on: K Means Clustering

MODULE 6: ML ALGO: DECISION TREE

• Decision Tree and How it works
• Hands-on: Decision Tree with ML Tool

MODULE 7: ML ALGO: SUPPORT VECTOR MACHINE (SVM)

• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Hands-on: SVM with ML Tool

MODULE 8: ARTIFICIAL NEURAL NETWORK (ANN)

• Introduction to ANN, How It Works
• Back propagation, Gradient Descent
• Hands-on: ANN with ML Tool

MODULE 1: DATABASE INTRODUCTION

• DATABASE Overview
• Key concepts of database management
• CRUD Operations
• Relational Database Management System
• RDBMS vs No-SQL (Document DB)

MODULE 2: SQL BASICS

• Introduction to Databases
• Introduction to SQL
• SQL Commands
• MY SQL workbench installation

MODULE 3: DATA TYPES AND CONSTRAINTS

• Numeric, Character, date time data type
• Primary key, Foreign key, Not null
• Unique, Check, default, Auto increment

MODULE 4: DATABASES AND TABLES (MySQL)

• Create database
• Delete database
• Show and use databases
• Create table, Rename table
• Delete table, Delete table records
• Create new table from existing data types
• Insert into, Update records
• Alter table

MODULE 5: SQL JOINS

• Inner join, Outer Join
• Left join, Right Join
• Self Join, Cross join
• Windows Functions: Over, Partition, Rank

MODULE 6: SQL COMMANDS AND CLAUSES

• Select, Select distinct
• Aliases, Where clause
• Relational operators, Logical
• Between, Order by, In
• Like, Limit, null/not null, group by
• Having, Sub queries

MODULE 7: DOCUMENT DB/NO-SQL DB

• Introduction of Document DB
• Document DB vs SQL DB
• Popular Document DBs
• MongoDB basics
• Data format and Key methods
• MongoDB data management

MODULE 1: BIG DATA INTRODUCTION

• Big Data Overview
• Five Vs of Big Data
• What is Big Data and Hadoop
• Introduction to Hadoop
• Components of Hadoop Ecosystem
• Big Data Analytics Introduction

MODULE 2: HDFS AND MAP REDUCE

• HDFS – Big Data Storage
• Distributed Processing with Map Reduce
• Mapping and reducing stages concepts
• Key Terms: Output Format, Partitioners, Combiners, Shuffle, and Sort

MODULE 3: PYSPARK FOUNDATION

• PySpark Introduction
• Spark Configuration
• Resilient distributed datasets (RDD)
• Working with RDDs in PySpark
• Aggregating Data with Pair RDDs

MODULE 4: SPARK SQL and HADOOP HIVE

• Introducing Spark SQL
• Spark SQL vs Hadoop Hive

MODULE 1: TABLEAU FUNDAMENTALS

• Introduction to Business Intelligence & Introduction to Tableau
• Interface Tour, Data visualization: Pie chart, Column chart, Bar chart.
• Bar chart, Tree Map, Line Chart
• Area chart, Combination Charts, Map
• Dashboards creation, Quick Filters
• Create Table Calculations
• Create Calculated Fields
• Create Custom Hierarchies

MODULE 2: POWER-BI BASICS

• Power BI Introduction
• Basics Visualizations
• Dashboard Creation
• Basic Data Cleaning
• Basic DAX FUNCTION

MODULE 3: DATA TRANSFORMATION TECHNIQUES

• Exploring Query Editor
• Data Cleansing and Manipulation:
• Creating Our Initial Project File
• Connecting to Our Data Source
• Editing Rows
• Changing Data Types
• Replacing Values

MODULE 4: CONNECTING TO VARIOUS DATA SOURCES

• Connecting to a CSV File
• Connecting to a Webpage
• Extracting Characters
• Splitting and Merging Columns
• Creating Conditional Columns
• Creating Columns from Examples
• Create Data Model

OFFERED DATA ANALYST COURSES IN SEATTLE

DATA ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN SEATTLE

The data analytics market size is expected to reach USD 329.8 Billion by 2030 at a growing CAGR rate of 29.9% according to an Acumen Research and Consulting report. The demand for data analysts in Seattle is likely to remain high in the future due to the increasing importance of data-driven decision-making and the growth of big data.

DataMites is a reputable provider of data analytics course in Seattle, and it has become known for offering the Certified Data Analyst course. This course is designed for beginners and intermediates and provides a comprehensive understanding of data analytics, covering various topics such as statistics, data science basics, visual analytics, data modeling, and predictive modeling. The curriculum is all-inclusive, meaning that it doesn't require any coding experience. The primary aim of the course is to equip students with the skills to identify useful insights from unstructured data and utilize them to make informed business decisions. The course prepares students for a career in data analytics by providing them with a specialized syllabus, mock tests, high-quality study materials, job placement, and internship programs. These resources enable students to meet the industry's requirements and succeed in the field of data analytics.

The Certified Data Analyst Course in Seattle is a six-month program that includes two months of live online instruction, two months of practical projects, and two months of internship experience. This structure allows students to apply their knowledge to real-world situations, increasing their chances of securing entry-level analytics positions. The course focuses on the entire data analysis process, from data cleaning to visualization, and is taught by highly qualified instructors with expertise in extracting valuable insights from raw data. Furthermore, the program has received approval from IABAC, a globally recognized organization. This recognition enhances the course's reputation and acceptance within the industry, giving students added credibility and increasing their chances of finding employment in the field of data analytics.

The future of data analysts in Seattle looks promising, with many opportunities for growth and development in this field. Data analysts may need to stay up to date with emerging technologies and tools, such as machine learning and artificial intelligence, to keep their skills relevant. Additionally, they may need to be skilled at working with large datasets, using statistical analysis and predictive modeling to extract insights and make recommendations. Join DataMites to get an in-depth understanding of the domain and get the right career direction.

Along with the data analyst courses, DataMites also provides python training, deep learning, data engineer, data analytics, r programming, mlops, artificial intelligence, machine learning and data science courses in Seattle.

ABOUT DATA ANALYST COURSE IN SEATTLE

Data analytics refers to the process of examining and interpreting data to derive insights, inform decision-making, and solve problems. It involves using various statistical and computational techniques to analyze data sets.

While data analytics and data science share some similarities, data analytics tends to focus more on the practical application of data and is more concerned with answering specific business questions. Data science, on the other hand, is a broader field that includes data analytics but also encompasses machine learning, data engineering, and other related disciplines

Data analytics is a career path that is open to anyone with the necessary skills and qualifications. Many organizations are actively seeking individuals with strong data analytics skills to help them make more informed decisions and drive business growth.

Some of the most essential skills for data analytics include proficiency in statistical analysis, data visualization, programming languages like Python or R, and database management. In addition, strong critical thinking and problem-solving abilities are also essential.

Commonly used tools and techniques in data analytics include data cleaning and preparation, exploratory data analysis, regression analysis, clustering, and classification. Popular software tools include Microsoft Excel, Tableau, Power BI, SAS, and Python/R libraries such as Pandas, NumPy, and Scikit-learn.

The fee would differ from institute to institute and the level of training you are looking for. The Data Analytics Training Fee in Seattle ranges from IDR 7195588 to IDR 16190074.18.

For individuals aspiring to pursue a career in the analytics industry, DataMites is the perfect option. The course structure is well-crafted, and the trainers are highly skilled and focused on industry-specific needs. DataMites provides hands-on training through internships and projects, enabling trainees to acquire practical experience.

Data analytics offers a wide range of employment opportunities across diverse industries, including healthcare, e-commerce, finance, and marketing. There are several popular job titles available in this field, such as data analyst, business analyst, data scientist, data engineer, and data architect, among other positions.

The application of data analytics can provide benefits to multiple industries and use cases, such as banking, marketing, healthcare, and manufacturing. The analysis of extensive and complex data sets involves various techniques and technologies, including data mining, machine learning, and predictive analytics.

The average salary of a data analyst in Seattle is around $ 80777 per year according to an Indeed report.

FAQ’S OF DATA ANALYST COURSE IN SEATTLE

DataMites provides top-notch certification training in data analytics in Seattle that enables individuals to showcase their proficiency in the field. This program prepares individuals to assist organizations in comprehending data and making informed decisions, which can lead to career opportunities with prominent multinational companies. Obtaining a certification from DataMites signifies an individual's capacity to execute specific job responsibilities in compliance with professional standards, making it a more valuable certification compared to a basic data analytics certificate.

DataMites' Certified Data Analyst Course, available in Seattle, is an excellent choice for individuals who aspire to pursue a career in data analytics or data science. This course is perfect for beginners, as it is a no-coding program that does not require any prior programming experience. The course curriculum is designed in a systematic manner to provide a comprehensive understanding of the subject matter. If you are interested in analytics and eager to explore this field further, enrolling in this course is a great way to enhance your knowledge.

Your greatest option in the field is the data analyst certification training from DataMites. We provide you with tangible proof that you are qualified to help companies, including well-known multinationals, interpret the data at hand through our data analytics training. It is evidence that, in contrast to a data analytics certificate, you are qualified to carry out the responsibilities of a particular job role in accordance with professional standards.

DataMites' Certified Data Analyst Training is an outstanding choice because of its no-coding syllabus that doesn't need prior programming knowledge, and its all-inclusive training program that offers practical experience in the realm of data analytics.

The fee would differ from institute to institute and the level of training you are looking for. The Data Analytics Training Fee in Seattle ranges from USD 600 to USD 1,600.

You will receive six months of data analytics training from DataMites, including 20 hours of instruction every week.

If you aspire to pursue a career as a data analyst, enrolling in DataMites' Certified Data Analyst Training is a wise decision. The curriculum is designed to provide individuals with comprehensive knowledge, hands-on experience, and industry-recognized certifications, enabling them to kickstart their data analyst career from scratch with confidence.

With our Flexi-Pass for the Certified Data Analytics Training, candidates have a three-month window in which to attend sessions from DataMites pertaining to any question or revision they wish to clear.

You can pay us in cash, with a debit card, a check, a credit card, PayPal, a Visa, Mastercard, or American Express card, as well as through net banking.

Yes, We will grant you IABAC® accreditation, ensuring that the relevant abilities are acknowledged internationally.

The DataMites Placement Assistance Team(PAT) facilitates the aspirants in taking all the necessary steps in starting their career in Data Science. Some of the services provided by PAT are: -

  • 1. Job connect
  • 2. Resume Building
  • 3. Mock interview with industry experts
  • 4. Interview questions

The DataMites Placement Assistance Team(PAT) conducts sessions on career mentoring for the aspirants with a view of helping them realize the purpose they have to serve when they step into the corporate world. The students are guided by industry experts about the various possibilities in the Data Science career, this will help the aspirants to draw a clear picture of the career options available. Also, they will be made knowledgeable about the various obstacles they are likely to face as a fresher in the field, and how they can tackle.

No, PAT does not promise a job, but it helps the aspirants to build the required potential needed in landing a career. The aspirants can capitalize on the acquired skills, in the long run, to a successful career in Data Science.

View more

OTHER DATA ANALYST TRAINING CITIES IN USA

Global DATA ANALYTICS COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog