CERTIFIED DATA ANALYST CERTIFICATION AUTHORITIES

COURSE FEATURES

DATA ANALYTICS LEAD MENTORS

DATA ANALYST COURSE FEES IN TORONTO

Live Virtual

Instructor Led Live Online

C 2,890
C 1,639

  • 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

C 1,450
C 942

  • 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 TORONTO

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 TORONTO

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 TORONTO

DATA ANALYST TRAINING COURSE REVIEWS

ABOUT DATAMITES DATA ANALYST TRAINING IN TORONTO

According to Market Research Future, the big data analytics market reached a valuation of USD 240.56 billion in 2021 and is estimated to expand at a CAGR of 13.4% between 2022 and 2029, with its worth increasing from USD 271.83 billion in 2022 to USD 655.53 billion by 2029.

DataMites is a leading institute for data analytics training, offering a comprehensive Certified Data Analyst Course in Toronto that covers all essential concepts and tools in data analysis. Students can also earn the globally recognized IABAC certification for data analysts to advance their careers. Additionally, DataMites provides an online certified data analyst course in Canada that provides students with the flexibility to learn at their own pace, and students can gain practical experience through valuable internship opportunities.

Toronto, Canada's largest city, is a major hub for data analytics. The city has a vibrant ecosystem of startups, established companies, and academic institutions that specialize in data analytics, artificial intelligence, and machine learning. Toronto's government has also recognized the importance of the data analytics industry and has created initiatives to promote its growth, including funding for research and development and the establishment of innovation hubs. The city's diversity, talent pool, and business-friendly environment have made it an attractive destination for data analytics companies looking to grow and innovate.

Attaining a data analytics course in Toronto can be a wise investment for those looking to establish a career in the field of data analytics. Data analytics is an exciting field with immense career potential, and the DataMites Certified Data Analyst Training in Toronto can help you achieve your career goals. With a focus on practical learning, this program can equip students with the essential skills and knowledge required to become successful data analysts. Don't hesitate - register today and take the first step towards building a successful career in data analytics.

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 Toronto.

ABOUT DATA ANALYST COURSE IN TORONTO

Data analytics is a key component of business intelligence, providing valuable insights into business operations and performance.

Yes, anyone can pursue a profession in data analytics. However, a strong foundation in mathematics, statistics, and computer programming is necessary to excel in this field.

Essential competencies required for data analytics include proficiency in mathematics, statistics, and computer programming. Additionally, individuals need to possess analytical skills, attention to detail, problem-solving ability, and the capacity to manage big data. Collaboration and communication skills are also significant for success in the field.

Data analytics can be classified into three types: descriptive, predictive, and prescriptive analytics. Descriptive analytics focuses on analyzing past data to understand past occurrences. Predictive analytics employs statistical methods and machine learning algorithms to forecast future events based on historical data. Prescriptive analytics utilizes various optimization techniques to determine the best course of action to achieve a particular objective.

SQL, Python, R, Excel, and Tableau are some of the commonly used tools in data analytics. Additionally, techniques such as data visualization, statistical analysis, predictive modeling, and machine learning are also frequently employed. Other techniques may include data cleaning, data transformation, and data integration.

The cost of certified data analytics training in Toronto typically ranges from 1200 CAD to 2200 CAD, depending on the mode of training selected.

DataMites is a highly respected institution in Toronto that offers data analytics training, with a range of programs and courses that meet students' needs. With an emphasis on hands-on learning and real-world projects, DataMites' courses are designed to provide students with the skills they need to succeed in the field of data analytics.

Data analytics is a fast-growing field, and the demand for skilled data analysts is on the rise. A career in data analytics offers individuals a diverse range of job opportunities across various industries and functions. Data analysts are in high demand in sectors such as healthcare, finance, retail, and technology, among others. With a combination of technical expertise, problem-solving abilities, and business acumen, individuals can establish a rewarding career in data analytics with opportunities for career advancement.

For those interested in learning data analytics, the DataMites Certified Data Analyst Course in Toronto is a top-notch option. The course offers a comprehensive curriculum that covers everything from programming languages to machine learning, providing students with the knowledge and skills necessary to excel in the field.

According to glassdoor.com, the average salary for a data analyst in Toronto is 59,331 CAD a year.

FAQ’S OF DATA ANALYST COURSE IN TORONTO

The data analytics course offered by DataMites provides a comprehensive curriculum covering all the essential concepts and skills required for a career in data analytics, delivered by experienced instructors who offer industry-relevant training. With practical exposure through hands-on experience with real-world datasets, the course offers a flexible learning experience with a Flexi-Pass option, affordable fees with payment options, internship opportunities, and a globally recognized certification approved by IABAC.

Absolutely, a free demo class will be arranged for you to give you an overview of the training approach and what it entails, prior to your payment of the course fee.

The DataMites Certified Data Analytics Course in Toronto is a six-month program that provides students with 20 hours of instruction per week.

No, DataMites® does not provide classroom training for Data Analytics in Toronto as their physical classrooms are only located in India. However, they offer online courses for the same.

The DataMites Certified Data Analyst Program is an excellent starting point for beginners who want to work in the field of data science or data analytics, even if they lack coding skills. The program covers all the basics of the subject, making it an ideal choice for those who want to learn analytics. Enroll now in the Data Analytics Training program offered by DataMites in Toronto.

DataMites offers certified data analytics training at varying prices depending on the type of training preferred. Typically, in Toronto, a certified data analytics course may cost anywhere from 1137 CAD to 2110 CAD.

DataMites offers multiple payment methods to pay for their Certified Data Analytics Course in Toronto, including cash, debit cards, checks, credit cards like Visa, Mastercard, American Express, and online payment systems such as PayPal and net banking.

After taking the online exam at exam.iabac.org, the results will be provided instantly, and it usually takes 7-10 business days to receive the e-certificate from IABAC. This time frame is based on the guidelines provided by IABAC.

DataMites' Flexi-Pass is an advantageous feature that enables students to attend their course sessions at their convenience. This feature offers access to both live and recorded classes, which are valid for a specific period from the enrollment date. It is especially helpful for individuals with busy schedules or work commitments that prevent them from attending regular classes.

After completing DataMites' Certified Data Analytics Training in Toronto, students receive IABAC® certification, which is globally recognized and validates their expertise and knowledge in data analytics. IABAC is a professional organization that provides internationally recognized certification programs for data analysts, business analysts, and data scientists. Passing their certification exam is an asset for data analysts.

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 CANADA

Global DATA ANALYTICS COURSES Countries

popular career ORIENTED COURSES

DATAMITES POPULAR COURSES


HELPFUL RESOURCES - DataMites Official Blog