Road map of Data Science

 A.Comparison and contrast of Data Science

i) Scope: Data Science combines expertise from several domains like as Mathematics,Statistics,Compuer Science ,Domain specific knowledge,Business Analytics knowledge,specifically support discipline,decision-making and improve business performance-based

ii) Techniques and Tools :1.Excel 2.Power BI 3.SQL 4.Python 5.ML 6.DL 7.AI 8.R 9.Statistics for DS 10.Mathematics for DS

iii) Goals :

B.Step of Data Science Projects

1.Data Collection

2.Data Cleaning and preprocessing

3.Exploratory Data Analytisis (EDA)

4.Feature Engineering

5.Model Development

6.Model Evaluation and validation

7.Interpretion and Communication

C.Business Analytics

i) Descrptive Analytics

ii) Diagnostic Analytics

iii) Pradictive Analytics

iv) Prescriptive Analytics

Module of Data Science

Module-1.Data Process,Data Storage and Management ( Tools)

1.1.Excel

1.2.SQL

1.3.NoSQL

Module-2.Data Visualization and Graphics

2.1.Power BI

2.2.Tablue

Module-3.Mathematics for Data Science

3.1.Basic Arithmetic

3.2. Basic Algebra

3.3. Basic Geometry

3.4. Basic Trigonometry

3.5.Vactors

3.6. Matrices

Module -4.Statistics for Data ,Data Exploration and Hypothesis Testing in Data Science

Module-5.Data Strutures and Librarires

Programming Languages

5.1.Python

5.2.R

Module-6.Machine learning Libraries

6.1.Scikit-Learn

6.2.Tensor Flow

Module-7.Cloud Computing Platforms

7.1AWS

7.2.Azure

7.3.Google Cloud

Module-8.Domain Expertise

Module-9.Communication and Storytelling

Module-10.Real Life Case Studies

10.1.Marketing and Customer Analytics

10.2. Supply chain Optimization

10.3.Fraud Detaction and Risk Management

10.4. Healthcear and Personalized Medicine

10.5.Human Resources and Talent Management

Module-11.

Steps of Data Science Projects

1.Data Collection

2.Data Cleaning and preprocessing

3.Exploratory Data Analysis (EDA)

4.Feature Engineering

5.Model Development

6.Model Evaluation and validation

7.Interpretation and Communication

Field :*Data Analytics to **Data Science

1.Excel

2.Database:

SQL:

• Microsoft SQL server
• MySQL 
• Oracle
• PostgreSQL

 NoSQl:

*MongoDB

*MariaDB

3.Data visualisation Tools: powerbi,Tableau,Looker Studio,Qlik Sense, QlikView,Excel VBA

4.Programming Language:

• Python
• R
• Data Structure
• Time Complexity
• Web Scraping
• Linux
• Git

Data Science

5.Statistics:

• Introduction to Statistics
• Data Description
• Random Samples
• Sampling Distribution
• Parameter Estimation
• Hypotheses Testing (Python | R)
• ANOVA (Python | R)
• Reliability Engineering
• Stochastic Process
• Computer Simulation
• Design of Experiments
• Simple Linear Regression
• Correlation
• Multiple Regression (Python | R)
• Nonparametric Statistics
  • Sign Test
  • The Wilcoxon Signed-Rank Test (R)
  • The Wilcoxon Rank Sum Test
    • The Kruskal-Wallis Test (R)
  • Statistical Quality Control
  • Basics of Graphs

6.Mathematics: 

• Linear Algebra
• Analytics Geometry
• Matrix
• Vector
• Calculus
• Optimization
• Regression
• Classification
• Density estimation
• Dimensionality Reduction

Probability:

• Data Cleaning & Exploration:
• Numpy,
• Pandas,
• Matplotlib,
• Seaborn,
• scikit learn

8.EDA: 

   Exploratory Data Analysis

9.Machine Learning:

      Introduction:

• How model works
• Basic Data Exploration
• Model building
• Model validation
• Underfitting
• Overfitting
• Random Forest
• Scikit Learn

Intermediate:

• Handling Missing Values
• Handling Categorical variables
• Pipelines
• Cross-Validation
• XGBoost
• Data Leakage

10.Deep Learning:

• ANN-Artificial Neural network
• CNN-Convolutional Neural network
• Reras
• PyNN-Recurrent Neural network
• KTorch
• TensorFlow
• A single Neuron
• Deep Neural network
• Underfitting
• Overfitting
• Dropout Batch Normalisation
• Binary Classification
• Stochastic Gradient Descent

11.AIFeature Engineering:

• Baseline Model
• Categorical Encoding
• Feature Generation

Feature Selection

12:Artificial Intelligence

13.NLP-Natural language Processing

• Text Classification 
• Word Vectors

14.Deployment:

• Microsoft Azure
• Heroku
• Google Cloud Platform
• Glask
• Django

15.Other Points:

• Domain Knowledge
• Reinforcement Learning
• CCommunication Skills
• ase Ses
•    Data science at Netuditflix

•    Data science at FlipKart
• Project on Movie Recommendation
• Project on Fraud Detection
• Project on Credit Card

Data Scientist vs Data Analyst

Here is a quick comparison of Data Scientist and Data Analyst

Aspect	Data Scientist	Data Analyst
Scope	Broader focus: machine learning, predictive modelling.	Focus: analysing data, and providing insights.
Focus	Uncovering patterns, and predicting trends.	Summarising historical data, providing insights.
Responsibilities	End-to-end processes, complex models.	Proficient in tools, statistical methods, and reporting.
Tools	Advanced: machine learning, Python/R.	Tools: Excel, Tableau, Power BI.
Data Types	Structured, unstructured, large datasets.	Primarily structured data, occasional smaller sets.
Outcome	Extract actionable insights, and solve complex problems.	Summarise data, and provide insights for decision-making.
Overlap	Some overlap and Analysts contribute to the early stages.	Distinct roles, potential for collaboration.