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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.
MODULE 1: PYTHON BASICS
• Introduction of python
• Installation of Python and IDE
• Python objects
• Python basic data types
• Number & Booleans, strings
• Arithmetic Operators
• Comparison Operators
• Assignment Operators
• Operator’s precedence and associativity
MODULE 2: PYTHON CONTROL STATEMENTS
• IF Conditional statement
• NESTED IF
• Python Loops basics
• WHILE Statement
• FOR statements
• BREAK and CONTINUE statements
MODULE 3: PYTHON DATA STRUCTURES
• Basic data structure in python
• String object basics and inbuilt methods
• List: Object, methods, comprehensions
• Tuple: Object, methods, comprehensions
• Sets: Object, methods, comprehensions
• Dictionary: Object, methods, comprehensions
MODULE 4: PYTHON FUNCTIONS
• Functions basics
• Function Parameter passing
• Generator functions
• Lambda functions
• Map, reduce, filter functions
MODULE 5: PYTHON NUMPY PACKAGE
• NumPy Introduction
• Array – Data Structure
• Core Numpy functions
• Matrix Operations
MODULE 6: PYTHON PANDAS PACKAGE
• Pandas functions
• Data Frame and Series – Data Structure
• Data munging with Pandas
• Imputation and outlier analysis
MODULE 1: DATA SCIENCE ESSENTIALS
• Introduction to Data Science
• Data Science Terminologies
• Classifications of Analytics
• Data Science Project workflow
MODULE 2: DATA ENGINEERING FOUNDATION
• Introduction to Data Engineering
• Data engineering importance
• Ecosystems of data engineering tools
• Core concepts of data engineering
MODULE 3: PYTHON FOR DATA SCIENCE
• Introduction to Python
• Python Data Types, Operators
• Flow Control statements, Functions
• Structured vs Unstructured Data
• Python Numpy package introduction
• Array Data Structures in Numpy
• Array operations and methods
• Python Pandas package introduction
• Data Structures : Series and DataFrame
• Pandas DataFrame key methods
MODULE 4: VISUALIZATION WITH PYTHON
• Visualization Packages (Matplotlib)
• Components Of A Plot, Sub-Plots
• Basic Plots: Line, Bar, Pie, Scatter
• Advanced Python Data Visualizations
MODULE 5: R LANGUAGE ESSENTIALS
• R Installation and Setup
• R STUDIO – R Development Env
• R language basics and data structures
• R data structures , control statements
MODULE 6: STATISTICS
• Descriptive And Inferential statistics
• Types Of Data, Sampling types
• Measures of Central Tendencies
• Data Variability: Standard Deviation
• Z-Score, Outliers, Normal Distribution
• Central Limit Theorem
• Histogram, Normality Tests
• Skewness & Kurtosis
• Understanding Hypothesis Testing
• P-Value Method, Types Of Errors
• T Distribution, One Sample T-Test
• Independent And Relational T Tests
• Direct And Indirect Correlation
• Regression Theory
MODULE 7: MACHINE LEARNING INTRODUCTION
• Machine Learning Introduction
• ML core concepts
• Unsupervised and Supervised Learning
• Clustering with K-Means
• Regression and Classification Models.
• Regression Algorithm: Linear Regression
• ML Model Evaluation
• Classification Algorithm: Logistic Regression
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 REGRESSION
• Introduction to Linear Regression
• How it works: Regression and Best Fit Line
• Modeling and Evaluation in Python
MODULE 3: ML ALGO: LOGISTIC REGRESSION
• Introduction to Logistic Regression
• How it works: Classification & Sigmoid Curve
• Modeling and Evaluation in Python
MODULE 4: ML ALGO: KNN
• Introduction to KNN
• How It Works: Nearest Neighbor Concept
• Modeling and Evaluation in Python
MODULE 5: ML ALGO: K MEANS CLUSTERING
• Understanding Clustering (Unsupervised)
• K Means Algorithm
• How it works : K Means theory
• Modeling in Python
MODULE 6: PRINCIPLE COMPONENT ANALYSIS (PCA)
• Building Blocks Of PCA
• How it works: Finding Principal Components
• Modeling PCA in Python
MODULE 7: ML ALGO: DECISION TREE
• Random Forest Ensemble technique
• How it works: Bagging Theory
• Modeling and Evaluation in Python
MODULE 8: ML ALGO: NAÏVE BAYES
• Introduction to Naive Bayes
• How it works: Bayes' Theorem
• Naive Bayes For Text Classification
• Modeling and Evaluation in Python
MODULE 9: GRADIENT BOOSTING, XGBOOST
• Introduction to Boosting and XGBoost
• How it works: weak learners' concept
• Modeling and Evaluation of in Python
MODULE 10: ML ALGO: SUPPORT VECTOR MACHINE (SVM)
• Introduction to SVM
• How It Works: SVM Concept, Kernel Trick
• Modeling and Evaluation of SVM in Python
MODULE 11: ARTIFICIAL NEURAL NETWORK (ANN)
• Introduction to ANN
• How It Works: Back prop, Gradient Descent
• Modeling and Evaluation of ANN in Python
MODULE 12: ADVANCED ML CONCEPTS
• Adv Metrics (Roc_Auc, R2, Precision, Recall)
• K-Fold Cross-validation
• Grid And Randomized Search CV In Sklearn
• Imbalanced Data Set: Smote Technique
• Feature Selection Techniques
MODULE 1: TIME SERIES FORECASTING - ARIMA
• What is Time Series?
• Trend, Seasonality, cyclical and random
• Autoregressive Model (AR)
• Moving Average Model (MA)
• Stationarity of Time Series
• ARIMA Model
• Autocorrelation and AIC
MODULE 2: FEATURE ENGINEERING
• Introduction to Features Engineering
• Transforming Predictors
• Feature Selection methods
• Backward elimination technique
• Feature importance from ML modeling
MODULE 3: SENTIMENT ANALYSIS
• Introduction to Sentiment Analysis
• Python packages: TextBlob, NLTK
• Case study: Twitter Live Sentiment Analysis
MODULE 4: REGULAR EXPRESSIONS WITH PYTHON
• Regex Introduction
• Regex codes
• Text extraction with Python Regex
MODULE 5: ML MODEL DEPLOYMENT WITH FLASK
• Introduction to Flask
• URL and App routing
• Flask application – ML Model Deployment
MODULE 6: ADVANCED DATA ANALYSIS WITH MS EXCEL
• MS Excel core Functions
• Pivot Table
• Advanced Functions (VLOOKUP, INDIRECT..)
• Linear Regression with EXCEL
• Goal Seek Analysis
• Data Table
• Solving Data Equation with EXCEL
• Monte Carlo Simulation with MS EXCEL
MODULE 7: AWS CLOUD FOR DATA SCIENCE
• Introduction of cloud
• Difference between GCC, Azure, AWS
• AWS Service ( EC2 and S3 service)
• AWS Service (AMI), AWS Service (RDS)
• AWS Service (IAM), AWS (Athena service)
• AWS (EMR), AWS, AWS (Redshift)
• ML Modeling with AWS Sage Maker
MODULE 8: AZURE FOR DATA SCIENCE
• Introduction to AZURE ML studio
• Data Pipeline and ML modeling with Azure
MODULE 1: GIT INTRODUCTION
• Purpose of Version Control
• Popular Version control tools
• Git Distribution Version Control
• Git Workflow
• Git Architecture
MODULE 2: GIT REPOSITORY and GitHub
• Git Repo Introduction
• Create New Repo with Init command
• Copying existing repo
• Git user and remote node
• Git Status and rebase
• Review Repo History
• GitHub Cloud Remote Repo
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
MODULE 5: UNDOING CHANGES
• Editing Commits
• Commit command Amend flag
• Git reset and revert
MODULE 6: GIT WITH GITHUB AND BITBUCKET
• Creating GitHub Account
• Local and Remote Repo
• Collaborating with other developers
• Bitbucket Git account
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
• Hands-on Map Reduce task
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
• Working with Spark SQL Query Language
MODULE 5: MACHINE LEARNING WITH SPARK ML
• Introduction to MLlib Various ML algorithms supported by MLib
• ML model with Spark ML.
• Linear regression
• logistic regression
• Random forest
MODULE 6: KAFKA and Spark
• Kafka architecture
• Kafka workflow
• Configuring Kafka cluster
MODULE 1: BUSINESS INTELLIGENCE INTRODUCTION
• What Is Business Intelligence (BI)?
• What Bi Is The Core Of Business Decisions?
• BI Evolution
• Business Intelligence Vs Business Analytics
• Data Driven Decisions With Bi Tools
• The Crisp-Dm Methodology
MODULE 2: BI WITH TABLEAU: INTRODUCTION
• The Tableau Interface
• Tableau Workbook, Sheets And Dashboards
• Filter Shelf, Rows And Columns
• Dimensions And Measures
• Distributing And Publishing
MODULE 3: TABLEAU: CONNECTING TO DATA SOURCE
• Connecting To Data File , Database Servers
• Managing Fields
• Managing Extracts
• Saving And Publishing Data Sources
• Data Prep With Text And Excel Files
• Join Types With Union
• Cross-Database Joins
• Data Blending
• Connecting To Pdfs
MODULE 4: TABLEAU: BUSINESS INSIGHTS
• Getting Started With Visual Analytics
• Drill Down And Hierarchies
• Sorting & Grouping
• Creating And Working Sets
• Using The Filter Shelf
• Interactive Filters
• The Formatting Pane
• Trend Lines & Reference Lines
MODULE 5: DASHBOARDS, STORIES AND PAGES
• Dashboards And Stories Introduction
• Building A Dashboard
• Dashboard Objects
• Dashboard Formatting
• Dashboard Interactivity Using Actions
• Story Points
• Animation With Pages
MODULE 6: BI WITH POWER-BI
• Power BI basics
• Basics Visualizations
• Business Insights with Power BI
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
• import and export dataset
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
• Cross join
• Self join
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: ARTIFICIAL INTELLIGENCE OVERVIEW
• Evolution Of Human Intelligence
• What Is Artificial Intelligence?
• History Of Artificial Intelligence.
• Why Artificial Intelligence Now?
• Ai Terminologies
• Areas Of Artificial Intelligence
• Ai Vs Data Science Vs Machine Learning
MODULE 2: DEEP LEARNING INTRODUCTION
• Deep Neural Network
• Machine Learning vs Deep Learning
• Feature Learning in Deep Networks
• Applications of Deep Learning Networks
MODULE 3: TENSORFLOW FOUNDATION
• TensorFlow Installation and setup
• TensorFlow Structure and Modules
• Hands-On: ML modeling with TensorFlow
MODULE 4: COMPUTER VISION INTRODUCTION
• Image Basics
• Convolution Neural Network (CNN)
• Image Classification with CNN
• Hands-On: Cat vs Dogs Classification with CNN Network
MODULE 5: NATURAL LANGUAGE PROCESSING (NLP)
• NLP Introduction
• Bag of Words Models
• Word Embedding
• Language Modeling
• Hands-On: BERT Algorithm
MODULE 6: AI ETHICAL ISSUES AND CONCERNS
• Issues And Concerns Around Ai
• Ai And Ethical Concerns
• Ai And Bias
• Ai: Ethics, Bias, And Trust
Every modern-day enterprise works in close coordination with data, and structures all their important decisions based on the valuable insights derived from it. Jobs in Data Science has grown to significant numbers and continues to be an exciting opportunity for the young population across the world. As per the report by the U.S. Bureau of Labour Statistics, the total number of jobs in Data Science is expected to rise to 11.5 million in the next 5 years. According to IBM, there will be around 700000 new job opportunities generated in the field of Data Science.
The Data Science course in New York City will help you to learn and become an expert in areas like Machine Learning Algorithms, Naive Bayes’, K-Means Clustering etc.
The Certified Data Scientist, is a Data Science certification course offered by DataMites in New York City. The course covers topics like Data Science Foundation, Machine Learning Algorithms, Programming languages like Python and ‘R’, Deep Learning Foundation, Computer Vision. The Data Science online course is led by experienced instructors.
DataMites, in New York City, offers dual certification programs in collaboration with IABAC and IBM that are recognised on a global platform.. The courses are accredited to IABAC- Globally recognised body for Business Analytics and Data Science Certifications. A duration of 6 months with 120 Hrs of training.Faculties with rich industry experience, and intense subject matter expertise.Guidance for resume building, interview preparations, expanding networks for job possibilities.Internship opportunities, with 25 capstone projects and 1 live project.
DataMites training institute is providing 45% discount on online data science course fee in New York City, USA. It’s a limited time offer. For any further details, you can get in touch with our executives.
DataMites also provides training for Machine Learning, Deep Learning, Python, IoT, Python for Data Science, and the Artificial Intelligence courses in New York City.
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 New York City is 6 months, a total of 120 hours of training. The training sessions are provided on weekdays and weekends. You can opt between the two, as per your convenience.
The course fee for the Certified Data Scientist course in the U.S.A range from $799-$2000. DataMites offers the Data Science course in New York City at an affordable price of $1440.
Data Science is a vast subject for study, it is a mix of Statistics and Computer Science. DataMites in New York City, offers quality training sessions in Data Science, Artificial Intelligence, Machine Learning, etc. The data science courses provided by DataMites in New York City 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 New York City, an individual will be well equipped with the following:-
New York City 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 New York City 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 New York City. As an acknowledgement to this rising demand, DataMites has come with the Certified Data Scientist course in New York City. 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.
New York City, in the U.S.A, 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, the U.S.A. is known for many recognised universities. Learning Data Science in the U.S.A 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.
New York City has several large companies, Banking and Financial institutions, Insurance companies, Automobile companies, Manufacturing enterprises, as a result, New York City happens to be the most sought after city when it comes to career opportunities in Data Science.
New York City 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 New York City.
As per the reports published by Indeed.com , the average salary of Data Scientists in New York City is $131,559 annually.
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 New York City 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 New York City 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.
The course fee for Data Science courses in the U.S.A range from $799-$2000. DataMites offers a Data Science course in New York City at an affordable price of $1440.
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 New York City 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 New York City. 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 training sessions provided by DataMites in New York City are primarily online. However, classroom training can be made available if there is adequate demand.
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.
DataMites accepts all the online payments(Debit/Credit) through Razor pay. If you opt to pay through your credit card, there will be an EMI option. DataMites collect token advance during the time of registration and the remaining payment should be settled in full before the completion of the course.
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;-
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.
IABAC Global Certifications
Comprehensive : Math, Stats, Machine Learning, Python, R, Tableau
6-month | 400 Learning Hours
Internship | Job Assistance
IABAC Global Certifications
Comprehensive: Computer vision, NLP,
Deep Learning, Reinforcement Learning
6-Month | 500 Learning Hours
Internship | Job assistance