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Data Science & Analytics

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Data Science & Analytics

Data Science and Analytics are pivotal disciplines at the forefront of modern decision-making and innovation. At Sazan Consulting, we leverage these disciplines to extract meaningful insights from data, guiding businesses towards informed strategies and smarter operations. From predictive modeling to machine learning algorithms, our expertise empowers organizations to uncover trends, optimize processes, and drive sustainable growth in today’s data-driven world.

The training primarily focuses on enhancing industry-level skills for working in the Data Science and Data Engineering domains. It emphasizes practical exercises, hands-on projects, and real-world applications rather than theoretical concepts.

 
 
  • Duration 6 - 7 Weeks
  • Classes only on Weekends
It Training

Data Science & Analytics

Sazan Consulting’s Data Science and Analytics Training and Placement Program is designed to transform you into industry-ready working professional. Whether you’re just starting out or looking to upskill, this program will equip you with the tools, techniques, and hands-on experience needed to thrive in today’s data-driven world.

Our program focuses on building strong foundational skills including:

  • Python Programming
  • Data Wrangling with Pandas and NumPy
  • Data Visualization using Matplotlib and Seaborn

 

You’ll gain hands-on experience with machine learning, covering everything from foundational to advanced algorithms, as well as an introduction to deep learning techniques.

Program Outline

Pre-requisite for Program: Good communication skills, Microsoft Office

Job roles: Data Scientist, Machine Learning Engineer, Data Analyst,
Business Intelligence Analyst, AI Specialist, Research Scientist
(AI/ML), NLP Engineer, Analytics Consultant, Data Product Manager.

  • Duration 6 - 7 Weeks
  • Classes only on Weekends

  1. What is Data Science? Definition, Overview, and Role of a Data Scientist
    Understand the fundamentals—what data science is and how it fits into modern businesses.
    Data Science vs. Data Analytics vs. Business Intelligence
    Real-World Use Case: Airbnb’s Data Science for Price Optimization
    The Role of a Data Scientist Explore what data scientists do day-to-day, the skills they need, and how they impact decision-making.

    Why Learn Data Science? Importance of Data-Driven Decisions. Learn why data is at the heart of innovation and smarter business choices. Cross-Industry Applications
    Discover how data science is transforming Industry Applications (Finance, Healthcare, E-commerce, etc.)
    Case Study: How Netflix Uses Data Science to Enhance User Experience

    Data Science Workflow Data Collection, Preparation, Modeling, Evaluation, and Deployment
    Tools and Technologies (Python, R, SQL, Excel, etc.)  Data Science Workflow Step-by-Step Process

    • Data Collection
    • Data Preparation
    • Modeling
    • Evaluation
    • Deployment
    • Tools & Technologies
      Get hands-on experience with essential tools like Python, R, SQL, Excel, Jupyter Notebooks, and version control with Git.

Introduction to Python Programming Python Basics: Variables, Data Types, Control Structures. Data Structures: Lists, Dictionaries, Tuples, Sets Functions, Loops, and Conditionals  
NumPy for Numerical Computing
Arrays, Element-Wise Operations, Array Manipulation
Case Study: Simulating Data for Stock Market Predictions
Pandas for Data Manipulation
Data Frames, Series, Filtering, Merging, Grouping
Use Case: Analyzing Sales Data for Retail Companies

Data Wrangling: Learn how to clean, organize, and manipulate data using powerful libraries like Pandas and NumPy—essential skills for any data professional

Data Cleaning
Handling Missing Data, Duplicates, Outliers, and Inconsistent Data Tools: Pandas, NumPy, scikit-learn
Feature Engineering
Creating New Features, Encoding Categorical Variables, Scaling, and Normalization
Use Case: Building a Credit Risk Model for a Bank
Data Transformation
Log Transform, Binning, Polynomial Features
Use Case: House Price Prediction by Transforming Features for Better Accuracy

Data Visualization: Turn raw data into clear, compelling visual stories using tools like Matplotlib and Seaborn. Learn how to create graphs, charts, and dashboards that make an impact. Importance of Visualization in Data Science


Tools: Matplotlib, Seaborn


Exploratory Data Analysis (EDA)
Creating Histograms, Box Plots, Pair Plots, Heatmaps
Case Study: Visualization of Customer Churn Data for a Telecom Company
Advanced Visualization Techniques
Using Plotly and Tableau for Interactive Dashboards
Case Study: Building a Sales Dashboard for a Retail Company

Statistics & Probability: Understand the foundational math behind data analysis and machine learning. You'll explore key concepts in statistics and probability in a practical, easy-to-understand way.

Machine Learning: Get hands-on with real machine learning algorithms—from basic models to more advanced techniques. Learn how machines "learn" from data and how to apply these methods in real projects.
Supervised vs. Unsupervised Learning, Terminology, and Concepts

Advanced Machine Learning Explore advanced techniques: ensemble models, hyperparameter tuning, model evaluation, and cross-validation.


Decision Trees and Random Forests
Building Trees, Feature Importance, Overfitting
Use Case: Predicting Employee Attrition Using Random Forests


Gradient Boosting & XGBoost
Boosting Techniques, Hyperparameter Tuning
Use Case: Predicting Loan Default Using XGBoost


Support Vector Machines
Concepts, Kernels, and Hyperplane
Use Case: Image Classification Using SVM

Introduction to Deep Learning
Dive into the world of neural networks and discover the basics of deep learning. You'll get a beginner-friendly look at how these powerful models work.


Introduction to Neural Networks
Structure of a Neural Network, Forward and Backpropagation
Use Case: Handwritten Digit Classification Using Neural Networks


Convolutional Neural Networks (CNNs)
Convolutions, Pooling, Dropout, and Architectures (LeNet, VGG)
Use Case: Image Recognition for Retail Product Detection


Recurrent Neural Networks (RNNs) and LSTMs
Sequential Data, Long Short-Term Memory (LSTM) Use Case: Predicting Stock Prices Using LSTMs

Natural Language Processing (NLP)
Learn to analyze and interpret human language using NLP tools and techniques such as tokenization, sentiment analysis, and topic modeling.


Introduction to NLP
Tokenization, Stop Words, Lemmatization, and Stemming
Use Case: Sentiment Analysis of Movie Reviews


Text Vectorization
TF-IDF, Word2Vec, Embeddings
Use Case: Building a Text Classification Model for Spam Detection


Advanced NLP Techniques
BERT, GPT, Transformers
Use Case: Building a Chatbot Using Transformer Models

Big Data & Cloud Computing
Work with large-scale datasets using Spark, Hadoop, and cloud platforms like AWS, Azure, or Google Cloud.


Introduction to Big Data
Apache, Spark, Distributed Computing
Use Case: Processing Large Datasets in Financial Services

 

Data Science in the Cloud
AWS, Google Cloud, Azure for Data Science
Use Case: Deploying Machine Learning Models in AWS SageMaker

Discover how to deploy models to production and maintain them using MLOps best practices (CI/CD, monitoring, automation).


Real-World Projects: Work on case studies and projects based on real business scenarios.

Why Choose Us?

Job-Oriented Training – 6–7 weekends of interactive career-focused learning.
Hands-On Experience – Work with industry expert trainers and real- time projects.
Placement Assistance: Guidance and support for job readiness, including resume preparation and mock interviews.

Your Career Options After Training
• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Business Intelligence Analyst

  • Duration 6 - 7 Weeks
  • Classes only on Weekends

Program Outline

Pre-requisite for Program: Good communication skills, Microsoft Office

Job roles: Data Scientist, Machine Learning Engineer, Data Analyst,
Business Intelligence Analyst, AI Specialist, Research Scientist
(AI/ML), NLP Engineer, Analytics Consultant, Data Product Manager.

  • Duration 6 - 7 Weeks
  • Classes only on Weekends
  1. What is Data Science?

Definition, Overview, and Role of a Data Scientist
Data Science vs. Data Analytics vs. Business Intelligence
Real-World Use Case: Airbnb’s Data Science for Price Optimization

     2) Why Learn Data Science?

Importance of Data-Driven Decisions
Industry Applications (Finance, Healthcare, E-commerce, etc.)
Case Study: How Netflix Uses Data Science to Enhance User
Experience

     3) Data Science Workflow

Data Collection, Preparation, Modeling, Evaluation, and Deployment
Tools and Technologies (Python, R, SQL, Excel, etc.)

1) Introduction to Python Programming
Python Basics: Variables, Data Types, Control Structures
Data Structures: Lists, Dictionaries, Tuples, Sets Functions, Loops, and Conditionals

2) NumPy for Numerical Computing
Arrays, Element-Wise Operations, Array Manipulation
Case Study: Simulating Data for Stock Market Predictions

3) Pandas for Data Manipulation
DataFrames, Series, Filtering, Merging, Grouping
Use Case: Analyzing Sales Data for Retail Companies

1) Data Cleaning
Handling Missing Data, Duplicates, Outliers, and Inconsistent Data Tools: Pandas, NumPy, scikit-learn

2) Feature Engineering
Creating New Features, Encoding Categorical Variables, Scaling, and Normalization
Use Case: Building a Credit Risk Model for a Bank

3) Data Transformation
Log Transform, Binning, Polynomial Features
Use Case: House Price Prediction by Transforming Features for Better Accuracy

1) Introduction to Data Visualization
Importance of Visualization in Data Science
Tools: Matplotlib, Seaborn

2) Exploratory Data Analysis (EDA)
Creating Histograms, Box Plots, Pair Plots, Heatmaps
Case Study: Visualization of Customer Churn Data for a Telecom Company

3) Advanced Visualization Techniques
Using Plotly and Tableau for Interactive Dashboards
Case Study: Building a Sales Dashboard for a Retail Company

1) Descriptive Statistics
Measures of Central Tendency (Mean, Median, Mode)
Measures of Dispersion (Variance, Standard Deviation, Skewness, Kurtosis)

2) Probability Distributions
Normal Distribution, Poisson, Binomial, Uniform
Use Case: Predicting Sales Trends Using Probability Distributions

3) Hypothesis Testing
Null and Alternative Hypothesis, T-tests, Chi-Square, P-Values
Use Case: A/B Testing for Website Optimization

1) Introduction to Machine Learning
Supervised vs. Unsupervised Learning, Terminology, and Concepts

2) Supervised Learning Algorithms
Linear Regression, Logistic Regression
Use Case: Predicting House Prices Using Linear Regression

3) Unsupervised Learning Algorithms
Clustering (K-Means, Hierarchical), Dimensionality Reduction (PCA)
Use Case: Customer Segmentation Using K-Means Clustering

4) Model Evaluation
Train/Test Split, Cross-Validation, Metrics (Accuracy, Precision, Recall, F1-Score)
Use Case: Evaluating a Fraud Detection Model in Banking

1) Decision Trees and Random Forests
Building Trees, Feature Importance, Overfitting
Use Case: Predicting Employee Attrition Using Random Forests

2) Gradient Boosting & XGBoost
Boosting Techniques, Hyperparameter Tuning
Use Case: Predicting Loan Default Using XGBoost

3) Support Vector Machines
Concepts, Kernels, and Hyperplane
Use Case: Image Classification Using SVM

1) Introduction to Neural Networks
Structure of a Neural Network, Forward and Backpropagation
Use Case: Handwritten Digit Classification Using Neural Networks

2) Convolutional Neural Networks (CNNs)
Convolutions, Pooling, Dropout, and Architectures (LeNet, VGG)
Use Case: Image Recognition for Retail Product Detection

3) Recurrent Neural Networks (RNNs) and LSTMs
Sequential Data, Long Short-Term Memory (LSTM) Use Case: Predicting Stock Prices Using LSTMs

1) Introduction to NLP
Tokenization, Stop Words, Lemmatization, and Stemming
Use Case: Sentiment Analysis of Movie Reviews

2) Text Vectorization
TF-IDF, Word2Vec, Embeddings
Use Case: Building a Text Classification Model for Spam Detection

3) Advanced NLP Techniques
BERT, GPT, Transformers
Use Case: Building a Chatbot Using Transformer Models

1) Introduction to Big Data
Apache, Spark, Distributed Computing
Use Case: Processing Large Datasets in Financial Services

2) Data Science in the Cloud
AWS, Google Cloud, Azure for Data Science
Use Case: Deploying Machine Learning Models in AWS SageMaker

1) Introduction to MLOps
CI/CD for ML Models, Model Monitoring, and Management
Use Case: Deploying a Real-Time Fraud Detection Model in
Production using Docker and Kubernetes

2) Model Deployment Techniques
Flask, FastAPI, Docker, Kubernetes for Model Serving
Use Case: Building a REST API for a Prediction Model

1) Capstone Project
Choose a Real-World Data Science Problem (Predictive Analytics, NLP, or Computer Vision)
Full Pipeline: Data Collection, Cleaning, Modeling, Evaluation, and Deployment

2) Real-World Use Cases will be discussed in class.

Pre-requisite for Program:

  • Familiar with programming language like Python.
  • Familiar with SQL, NoSQL.
  • Basic understanding of Mathematics and Statistics.
  • Basic Git knowledge (optional).
  • Awareness of cloud resources like google colab.
  • Must be available for 8 hours class per week, and at least 2 hours

(FAQs) on Data Science & Analytics:

Data Science is an interdisciplinary field that combines statistics, computer science, and
domain knowledge to extract insights and knowledge from structured and unstructured
data.

Data Science is broader and includes advanced techniques like machine learning,
predictive modeling, and algorithms to derive actionable insights.


Data Analytics focuses on processing and analyzing historical data to help businesses
make informed decisions. In short, Data Science = Predicting the future with data. Data
Analytics = Understanding the past with data.

Data cleaning is crucial because raw data often contains errors, inconsistencies,
duplicates, and missing values. Cleaning the data helps improve the accuracy of
analyses, reduces biases in models, and enhances the overall quality of insights.

Data Analyst: Primarily focuses on querying databases, generating reports, and using
statistical methods to provide insights from data.


Data Scientist: In addition to the tasks of a data analyst, a data scientist builds machine
learning models, designs experiments, and applies advanced algorithms to predict
future trends or behaviors.

Data wrangling (also called data munging) involves cleaning, transforming, and
organizing raw data into a usable format for analysis. It’s a critical part of the data

Data visualization is the graphical representation of data. It helps stakeholders easily
understand trends, patterns, and insights, which aids in making data-driven decisions.


Sazan Consulting offers a comprehensive Data Science and Analytics training
program designed to equip individuals with the skills necessary for roles such as Data
Scientist, Machine Learning Engineer, Data Analyst, Data Product Manager & more.

Why Become a Data Scientist?

  • High-demand industry with global career opportunities.
  • Excellent salary packages & Career growth
    Call us 647-313-1970 | Visit our website | Email info@sazanconsulting.com
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