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MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Evolution of Data Science
• Big Data Vs Data Science
• Data Science Terminologies
• Data Science vs AI/Machine Learning
• Data Science vs Analytics
MODULE 2: DATA SCIENCE DEMO
• Business Requirement: Use Case
• Data Preparation
• Machine learning Model building
• Prediction with ML model
• Delivering Business Value.
MODULE 3: ANALYTICS CLASSIFICATION
• Types of Analytics
• Descriptive Analytics
• Diagnostic Analytics
• Predictive Analytics
• Prescriptive Analytics
• EDA and insight gathering demo in Tableau
MODULE 4: DATA SCIENCE AND RELATED FIELDS
• Introduction to AI
• Introduction to Computer Vision
• Introduction to Natural Language Processing
• Introduction to Reinforcement Learning
• Introduction to GAN
• Introduction to Generative Passive Models
MODULE 5: DATA SCIENCE ROLES & WORKFLOW
• Data Science Project workflow
• Roles: Data Engineer, Data Scientist, ML Engineer and MLOps Engineer
• Data Science Project stages.
MODULE 6: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• ML Workflow, Popular ML Algorithms
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 7: DATA SCIENCE INDUSTRY APPLICATIONS
• Data Science in Finance and Banking
• Data Science in Retail
• Data Science in Health Care
• Data Science in Logistics and Supply Chain
• Data Science in Technology Industry
• Data Science in Manufacturing
• Data Science in Agriculture
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
• Empirical Rule and Outliers
• Central Limit Theorem
• Normality Testing
• Skewness & Kurtosis
• Measures Of Distance: Euclidean, Manhattan And Minkowski Distance
• 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: MACHINE LEARNING INTRODUCTION
• What Is ML? ML Vs AI
• Clustering, Classification And Regression
• Supervised Vs Unsupervised
MODULE 2: PYTHON NUMPY PACKAGE
• Introduction to Numpy Package
• Array as Data Structure
• Core Numpy functions
• Matrix Operations, Broadcasting in Arrays
MODULE 3: PYTHON PANDAS PACKAGE
• Introduction to Pandas package
• Series in Pandas
• Data Frame in Pandas
• File Reading in Pandas
• Data munging with Pandas
MODULE 4: VISUALIZATION WITH PYTHON - Matplotlib
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
MODULE 5: PYTHON VISUALIZATION PACKAGE - SEABORN
• Seaborn: Basic Plot
• Advanced Python Data Visualizations
MODULE 6: ML ALGO: LINEAR REGRESSSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 7: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 9: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 1: FEATURE ENGINEERING
• Introduction to Feature Engineering
• Feature Engineering Techniques: Encoding, Scaling, Data Transformation
• Handling Missing values, handling outliers
• Creation of Pipeline
• Use case for feature engineering
MODULE 2: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 3: PRINCIPAL COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 4: ML ALGO: DECISION TREE
• Introduction to Decision Tree & Random Forest
• How it works
• Modeling and Evaluation in Python
MODULE 5: ENSEMBLE TECHNIQUES - BAGGING
• Introduction to Ensemble technique
• Bagging and How it works
• Modeling and Evaluation in Python
MODULE 6: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 7: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works?
• Modeling and Evaluation of in Python
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Stationarity of Time Series
• Autoregressive Model (AR)
• Moving Average Model (MA)
• ARIMA Model
• Autocorrelation and AIC
• Time Series Analysis in Python
MODULE 2: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• NLTK Package
• Case study: Sentiment Analysis on Movie Reviews
MODULE 3: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 4: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model deployment
MODULE 5: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Data Table
• Goal Seek Analysis
• Pivot Table
• Solving Data Equation with EXCEL
MODULE 6: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 instance)
MODULE 7: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline
• ML modeling with Azure
MODULE 8: INTRODUCTION TO DEEP LEARNING
• Introduction to Artificial Neural Network, Architecture
• Artificial Neural Network in Python
• Introduction to Convolutional Neural Network, Architecture
• Convolutional Neural Network in Python
MODULE 1: DATABASE INTRODUCTION
• DATABASE Overview
• Key concepts of database management
• Relational Database Management System
• CRUD operations
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 function: 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
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Terminologies
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Git Essentials: Copy & User Setup
• Mastering Git and GitHub
MODULE 3: COMMITS, PULL, FETCH AND PUSH
• Code Commits
• Pull, Fetch and Conflicts resolution
• Pushing to Remote Repo
MODULE 4: TAGGING, BRANCHING AND MERGING
• Organize code with branches
• Checkout branch
• Merge branches
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 5: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
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
AI is the future, and DataMites helped me step into it! The online Data Science course was easy to follow, and the tutors explained everything clearly. Even after completing my course, the team supported me with placements and I successfully cracked a job! Grateful to DataMites for shaping my career.
I joined DataMites to learn Data Science and gained strong skills in ML, DL, and Statistics. Through my internship at Rubixe AI Solutions, I worked on real-world projects, which helped me land job interviews and an offer. The trainers were very supportive and explained every concept clearly. Thanks to DataMites, my data science journey was smooth and successful.
My mentor, a DataMites alumnus, suggested I join the Data Science course, and it was one of my best decisions! The offline classes were interactive and full of guidance, and I got the chance to intern at Rubixe AI Solutions. The placement team was very supportive and helped me perfect my resume. Truly thankful to DataMites for this amazing learning journey.
I joined DataMites after completing my engineering in Computer Science and became a Certified Data Scientist. With their support, I completed an internship at Rubix AI Solutions and now work as an AI intern at Inventor Hub. DataMites made learning statistics and data science easy with clear guidance and great materials. I’m truly thankful for their support.
I joined DataMites for the Certified Data Scientist online course, and it was a great experience! The real-time projects helped me apply my learning practically, and the placement support added real value to my career restart after 8 years. DataMites is the perfect platform to learn, grow, and begin your data science journey.
I joined DataMites for the Data Science course and learned Python, SQL, Machine Learning, and Deep Learning through hands-on projects. The trainers are excellent at explaining concepts clearly. The placement team is very supportive and provides great opportunities thanks to DataMites for helping me start my data science journey.
I joined DataMites after my B.Tech when I developed an interest in Data Science and Machine Learning. The training was well-structured, and I even got an internship opportunity that helped me gain real experience. The placement team was super supportive and guided me through every step. Thanks to DataMites, I’m now placed at Horizon Broadband Pvt. Ltd.
I joined DataMites after completing my BCA, and it was the best decision for my data science career! I learned Python, SQL, Machine Learning, Deep Learning, and NLP, and worked on several capstone and client projects that gave me real-world experience. The trainers and team were always friendly and supportive, patiently clearing all my doubts. Thanks to DataMites, I’m confident and job-ready in data science
I started my data science journey with DataMites, and it’s been an amazing experience! The Certified Data Scientist Program helped me learn everything from scratch through hands-on projects and an internship. The placement team was incredibly supportive, always ready to clarify my doubts. Thanks to their guidance, I successfully got placed. Highly recommend DataMites to anyone starting their data science career
Enrolling at DataMites was one of the best decisions I made for my career. Coming from a freelance writing background with no prior technical experience, I was initially unsure about stepping into the field of data analytics. But DataMites made the transition smooth and achievable. I’d highly recommend them to anyone looking for a guided, industry-ready data analytics course. A true DataMites success story!
Data Science is the art of collecting, classifying, summarizing data sets, and deriving valuable insights from these data sets. These insights are used to take further decisions. Data Science has become instrumental in adding value to the business.
There are no mandatory prerequisites. However, basic knowledge of Statistics would be an added advantage.
The various business skills required, to become a Data Scientist are as follows:-
Industry Knowledge:- A Data Scientist should have a clear understanding of the areas that need to be paid attention and the areas that need to be ignored. This is possible only if the Data Scientist has sound knowledge of the industry.
Problem Solving Skills:- A Data Scientist is known for finding solutions to problems. For doing so, a Data Scientist must understand the problem, which can be achieved only after a deep study of the scenario.
Communication Skills:- A Data Scientist often needs to communicate the findings arrived at, with regards to analytics and business insights. A Data Scientist should be a good conversationalist.
Curiosity:- A Data Scientist should always be curious enough while approaching a problem. Finding out the root of the problem depends upon the curiosity of a Data Scientist.
As far as Data Scientist is concerned Python is the most effective programming language, with a lot of libraries available. Python can be deployed at every phase of data science functions. It is beneficial in capturing data and importing it into SQL. Python can also be used to create data sets.
Data Science is all about managing a set of information received from various sources, to arrive at conclusions. The data that is acquired needs to be analysed and decisions need to be taken. Statistics makes it easier to work on data. Various statistical techniques such as Classification, Regression, Hypothesis Testing, Time Series Analysis is used to construct data models. With the help of Statistics, a Data Scientist can gain better insights, which enables to effectively streamline the decision-making process.
The duration of the Data Science course in Mississauga is 8 months, a total of 700 hours of training. The training sessions are provided on weekdays and weekends. You can opt between the two, as per your convenience.
DataMites offers a Data Science course in Mississauga at an affordable price of C$ 85000.
Data Science is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in Mississauga, offers quality training sessions in Data Science, Artificial Intelligence, Machine Learning, etc. The data science courses provided by DataMites in Mississauga are exclusively designed in tune with the current industry requirements. Also with many projects to work on, under the mentoring of industry experts.
Whether you need a P.G degree to pursue a data science certification can be better understood, based on your knowledge in the Science & Technology, Engineering and Management domain. If you have a strong knowledge base in any of the mentioned areas
After completing the Certified Data Scientist Course in Mississauga, an individual will be well equipped with the following:-
Mississauga is known for lots of business opportunities and large corporate houses adorning the city. This, in turn, contributes to new employment opportunities being created. Hence opting for a Data Science course in Mississauga will help an individual to leverage the available possibilities in the best manner, to land a career in Data Science.
Data Scientists have been in great demand in Mississauga. As an acknowledgement to this rising demand, DataMites has come with the Certified Data Scientist course in Mississauga. The course covers all the areas of Data Science, Machine Learning, basics of Mathematics and Statistics, etc. Also, the Certified Data Scientist course, covers all the practical aspects of the knowledge required to become a Data Scientist.
Mississauga, in Canada, is known for a lot of business opportunities. It consists of many large companies, business houses, with large amounts of transactions happening every day, as a result of which there is an equally large amount of data generated daily. Also, Canada. is known for many recognised universities. Learning Data Science in Canada will be a great opportunity for students as well as professionals. Graduates freshers and employees working in organisations can leverage these opportunities to easily land a Data Science job.
Mississauga has several large companies, Banking and Financial institutions, Insurance companies, Automobile companies, Manufacturing enterprises, as a result, Mississauga happens to be the most sought after city when it comes to career opportunities in Data Science.
Mississauga is a city that is always bustling with business activities, financial transactions happening in huge volumes. Hence it serves to be a great opportunity for starting a Data Science Career in Mississauga.
As per the reports published by Indeed.com, the average salary of Data Scientists in Canada is SAR 180,000.
A large amount of data is being generated through various activities daily. For instance, data of investments done in the stock market, data of the financial transactions, data with regards to the browsing history. The company which you are associated with records and maintains your data. For example, when you make regular online purchases, the provider collects all the information on your activity and stores it securely. It then makes use of the same data to make further product recommendations. Different companies use data in different ways.
Data Science is all about the collection and classification of information and using the same to derive insights. Python and R are the two programming languages that are used in the data science process. Some of the reasons, for python being the most preferred programming language in comparison to R:-
The mode of training offered by DataMites for Data Science course in Mississauga is online training. However, classroom training can be made available, if there is adequate demand.
DataMites provides a range of courses in Data Science, Machine Learning, Artificial Intelligence,in Mississauga with training sessions uncompromised of quality, conducted by industry experts, professional data scientists who possess intense knowledge of the subject matter. The training is conducted in the online mode. The sessions are conducted based on case studies approach, with business cases taken up for discussion.
DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-
DataMites has a faculty of trainers who possess deep subject matter expertise and significant years of experience in the field of Data Science.
DataMites offers a Data Science course in Mississauga at an affordable price of C$ 2570.
The registrations cancelled within 48 hrs of enrollment will be refunded in full. The processing time of the refund is within 30 days, from the date of the receipt of cancellation request
Yes. You will receive a certificate from DataMites after the completion of the course.
DataMites in Mississauga offers dual certifications in collaboration with IABAC and IBM. IABAC is a global body, which offers certifications in Business Analytics and Data Science. IABAC is founded on the principles of EDISON Data Science Framework (EDSF). IBM provides the best in class industry certifications. DataMites provides a range of certifications in Data Science, Machine Learning, Artificial Intelligence. All the data science certifications offered by DataMites are structured based on the industry trends.
Enrolling for online training online is very simple. The payment can be done using your debit/credit card that includes Visa Card, MasterCard; American Express or via PayPal. You will receive the receipt after the payment is successful. In the case of more queries, you can get in touch with our educational counsellor who will guide you with the same.
You have access to the online study materials from 6 months up to 1 year.
DataMites offers online training in Mississauga. However, classroom training can also be made available, if there is adequate demand.
DataMites offers data science sessions, both on weekdays and weekends. You can opt between the two, based on your convenience.
DataMites offers data science sessions, in the Morning and Evening. You can opt, based on your convenience.
Yes. DataMites does provide an online lab facility. You can visit prolab.datamites.com. When you visit the site, it asks for the password, you must enter the password given to you, in order to access the facility.
Yes. DataMites do provide live data science projects, which are done under the guidance of industry experts.
The data science course offered by DataMites in Mississauga includes 25 capstone projects and 1 client project.
The training sessions provided by DataMites in Mississauga are primarily online. However, classroom training can be made available if there is adequate demand.
DataMites is a training provider that imparts quality training and upskilling in Data Science, for freshers who are data enthusiasts and professionals who wish to enhance their career possibilities. Above all DataMites offers the following;-
DataMites provides Flexi Pass, which gives you the privilege to attend unlimited batches in a year. The Flexi Pass is specific to one particular course. Therefore if you have a Flexi Pass for one particular course of your choice, you will be able to attend any number of sessions of that course. It is to be noted that a Flexi Pass is valid for a particular period.
All the online sessions are recorded and will be shared with the candidates. If you miss any of the online sessions, you can still have access to the recordings later.
Yes. The Datamites certification exam fee is included in the total course fee. Therefore once you are registered for a course, you are also eligible to attend the exam.
Yes. DataMites offers internship opportunities along with the course. You will be mentored by industry experts through the internship. Once the internship is completed, DataMites provides you with the internship certificate along with the experience certificate.
The DataMites Placement Assistance Team(PAT) helps the candidates to have an easy start in his/her career. The team offers services like Resume Building, Interview Preparation. The team will assist you in the following areas;-
Project Mentoring- 100 hrs Live mentoring in industry projects.
Interview Preparations- Mock Interview sessions.
Resume Support- Personal guidance in resume creation by professionals.
Doubt clearing sessions- Live doubt clearing sessions on
Job updates- Interview connects.
No, DataMites doesn’t guarantee a job, but it will provide all the support and guidance needed, in getting a job, Resume Building, Interview preparations. DataMites internships offer a candidate to work with industry experts, which helps in knowing the corporate way of working. This proves as a stepping stone to an individual’s professional life.
DataMites internship programs are exclusively designed for a candidate to enable him/her to get a practical experience of working on live projects. The candidate gets an opportunity to work under the guidance of industry experts.
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: -
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.
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