Pay later
Sign Up
Login
Pay Later

Professional Data Science Program

Data Science: The imminent frontier of technological advancement and innovation.

Enroll Now
data_learnmore
online-education
0 +

Live Training Hours
Online course
0 +

Hiring Partners
0 +

Tools Covered

Program Prerequisites

No Prior Coding experience required.

Laptop with Internet Connections.

Basic Knowledge
of Maths.
Basic Communication
skills.

Introduction to Excel for Data Analytics

data-analytics
Weekends
  • Basics of Excel
  • Statistical and Mathematical Functions in Excel
  • Lookup functions, Index and Matching
  • Logical, Text, Datetime functions
  • Data Cleaning and feature engineering
  • What if analysis and Scenario Management
  • Charts and Dashboards
  • Linear Regression and Forecasting
Weekdays Plan
  • W1: Introduction to Maths for Data Analytics
  • W2: Introduction to Inferential Statistics
  • W3: Introduction to Power Query
  • W4: Introduction to Macros

Introduction to Tableau for Data visualization and BI

Weekends
  • Basics of Tableau
  • Tableau Charts and Maps
  • Table Calculations and Calculated fields
  • Aggregations, Granularity, LOD Expressions
  • Data Extracts and Filters
  • Tableau Dashboards and Storyboards
Weekdays Plan
  • W1: Data Preparation in Power BI
  • W2: Data Modelling in Power BI
  • W3: Dashboarding in Power BI
Data visualization

Introduction to Python

Weekends
  • Basics of Python
  • Introduction to Data Structures
  • Introduction to Functions in Python
  • Introduction to Strings and Regular Expressions
  • Loops and Conditionals
  • OOPs, and Space-Time Complexity
Weekdays Plan
  • W1: Fundamentals of R Programming
  • W2: Statistical Functions in R Programming
  • W3: Introduction to GGplot in R Programming

Introduction to Statistics and Hypothesis Testing

Weekends
  • Introduction to Descriptive Statistics
  • Introduction to Basic and Conditional Probability
  • Introduction to Inferential Statistics
  • Introduction to Hypothesis Testing
Weekdays Plan
  • W1: EDA, and Data Preparation in SAS
  • W2: Using SQL Queries in SAS
Statistics and Hypothesis

Introduction to Data Analysis and Visualization

Data Analysis
Weekends
  • Introduction to Numpy and Pandas
  • Basic Charts in Python
  • Advanced and Interactive Charts in Python
  • Case Study1 - Data Analysis and Visualization
Weekdays Plan
  • W1: Introduction to Query Analysis using Pandas
  • W2: Introduction to Plotly and Express

Introduction to Data Cleaning/Preparation/Processing

Weekends
  • Introduction to Missing Values and Outliers
  • Categorical Encoding Techniques
  • Data Transformations and Feature Engineering
  • Data Splitting, and Feature Scaling
  • Case Study2: Data Cleaning & Processing
Weekdays Plan
  • W1: Case Study1: Data Analysis and Visualization
  • W2: Case Study2: Data Cleaning and Processing
  • W3: Case Study3: Statistical Distributions
Data Cleaning

Introduction to Supervised Learning

Supervised-Learning
Weekends
  • Introduction to Linear Regression and Regularization
  • Introduction to Logistic Regression
  • Introduction to KNN, and SVM
  • Introduction to Decision Trees and Random Forests
  • Introduction to Boosting Algorithms
  • Introduction to Imbalanced Machine Learning
  • Advanced Modelling Techniques and Improvements
  • Project1: End-to-End ML Project. Dataset: Employee Attrition Dataset.
Weekdays Plan
  • W1: Introduction to Regularization Techniques
  • W2: Implementation of Regression Algorithms in Real-world Datasets
  • W3: Implementation of Classification Algorithms in Real-world Datasets
  • W4: Advanced Modelling Techniques.

Introduction to Unsupervised Learning

Weekends
  • Introduction to K Means and Hierarchical Clustering
  • Introduction to PCA and LDA
  • Project2: Clustering Analysis Dataset: Mall Customers segmentation Dataset
  • Casestudy3: Dimensionality Reduction
Weekdays Plan
  • W1: Evaluation Metrics for Unsupervised Learning
  • W2: Case Study 4: Dimensionality Reduction
Unsupervised Learning

Introduction to Time series forecasting and Recommender systems

forecasting-and-Recommender
Weekends
  • Time Series Fundamentals
  • Introduction to AR, MA, ARMA Models
  • Introduction to ARIMA and SARIMA
  • Introduction to ARIMAX and SARIMAX
  • Project3: Time Series Forecasting Dataset: Sales Forecasting Dataset
  • Project4: Recommendation Engine Dataset: Movie Recommendation Dataset
Weekdays Plan
  • W1: Implementation of Time Series algorithms on Real-world Datasets
  • W2: Implementation of Recommender Systems on Real-world Datasets

Introduction to Big Data and SQL

Weekends
  • Database Fundamentals
  • DQL, DDL, and DML Queries
  • SQL Joins
  • SQL sub-queries and set-operations
Weekdays Plan
  • W1: Fundamentals of MongoDB: Documents and Collections
  • W2: MongoDB: Replica sets, Sharding, and Indexes
big_data

Advanced Electives

Advanced effected
Weekends
  • Overview of Model Deployment & ML System Architecture
  • Packaging the ML Model for production
  • Serving and Deploying the Model via REST API
  • Continuous Integrations and Deployment Pipelines
  • Deploying ML API with containers & Differential Testing
  • Deploying to IaaS(AWS EC2)
Weekdays Plan
  • W1: Fundamentals of REST APIs.
  • W2: Fundamentals of AWS, AWS S3, and AWS EC2.
  • W3: Deployment of a Machine Learning Classification Model.

Natural Language Processing

Weekends
  • Introduction to NLP Basics
  • Part of Speech Tagging & Named Entity Recognition
  • Text Classification
  • Semantics & Sentiment Analysis
  • Topic Modeling
  • Text Summarization
Weekdays Plan
  • W1: Fundamentals of Text Data Cleaning
  • W2: Working on Text Classification problem
  • W3: Working on Topic Modeling problem

Deep Learning

deep learning
Weekends
  • Introduction to ANN
  • Introduction to CNN
  • Introduction to CNN Architectures
Weekdays Plan
  • W1: Fundamentals of Tensorflow
  • W2: Fundamentals of PyTorch

Program Outcomes

  • Depth understanding of R and Python Programming Language.
  • Ability to create solutions for data science problems using Python or R.
  • Depth understanding of Power BI and Tableau for BI.
  • Depth understanding of Databases and SQL.
  • Ability to perform Advanced Query Analysis on Databases.
  • Depth understanding of Advanced Excel for Data Analytics.
  • Ability to create Data Driven Dashboards using Excel/PowerBI/Tableau.
  • Depth understanding of Statistics and Hypothesis Testing.
  • Ability to work on any kind of Text/Image/Audio/Video/numerical Datasets.
  • Ability to perform Exploratory Data Analysis on any Dataset.
  • Ability to Clean, Process, and Prepare any dataset for Predictive Modelling.
  • Great understanding of Machine Learning, and Deep Learning Algorithms.
  • Ability to work with real-world dataset and apply ML/DL algorithms.
  • Great understanding of Natural Language Processing Concepts and Algorithms.
  • Ability to perform Time series forecasting using Time series Models.
  • Ability to create Recommender systems using content or collaborative filtering.
  • Depth understanding of Unsupervised Machine Learning algorithms.
  • Ability to perform Dimensionality Reduction on High dimensionality datasets.
  • Ability to perform Clustering analysis on any kind of datasets.

Program flow

8 Months

 Learn Python, R, Tableau, Excel, SQL, Power BI, Statistics, Data Science and Machine Learning with real-world Projects and Case-studies.

2 Months

Work on Domain Specialization Projects, Case studies, and Elective Subjects such Model Deployment, NLP, and Deep Learning.

1 Month

Become Job ready by preparing a Project Portfolio on Linkedin, Github, Hackerrank, Medium etc.

Fast Forward your career in Tech Fields with Meritshot's Best-in-class Training Programs.

Here are some steps you can take to accelerate your career in the technology industry

Explore Program
Enrol Now
Get In Touch

For Queries, Feedback or Assistance

Contact Us

Popular Programs

Interview Questions

Case Study

MERITSHOT ZETTA EDUTECH LLC 418 Broadway # 5572 Albany, NY 12207

©2024 Meritshot. All Rights Reserved.

loader