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Data Science is among the most prominent emerging careers in the 21st century. Data scientists are professionals responsible for gathering, evaluating, and interpreting large amounts of data.
Data Science is an umbrella term that encompasses data analytics, data mining, machine learning, and several other related disciplines. While a data scientist is expected to forecast the future based on past patterns, data analysts extract meaningful insights from various data sources.
Data Science is a concept used to tackle big data and includes data cleansing, preparation, and analysis. A data scientist gathers data from multiple sources and applies machine learning, predictive analytics, and sentiment analysis to extract critical information from the collected data sets. They understand data from a business point of view and can provide accurate predictions and insights that can be used to power critical business decisions.
Data Science is usually the person who can do basic descriptive statistics, turns unstructured data into Structured Data, Reporting for Data Visualization With Data Analysis Process and communicated at a points for conclusions.
Data Science must have a basic understanding of statistics, a perfect sense of databases, the ability to create new views, and the perception to visualize the data & get a clear idea of what to do & using that data to make critical ( more importantly, correct) decisions. Data analytics can be referred to as the necessary level of data science.
- Python Fundamentals
- Python Data Types & Resources
- Library Reference for Python's Many Standard Libraries
- Language Reference for Python's Syntax.
- Set Python Virtual Environment Setup
- Input Output Operations
- Functions & Modules in Python
- Exceptions Handling
- Advanced Oops Concepts in Python with Various Data types
- Working with Set, List, Dict. , Tuple Data, Interfaces in Python
- Packages & Library with Pip
- Python Database Connectivity Configurations
- Data Management & Manipulations (Storage, Fetch, Retrieve, Maintain)
- Implementation Of All Concept In Project Preparations
- Powerful Graphical User Interface Toolkit for Python
- Drag-and-Drop GUI builder, with built-in hosting for Our Apps.
- Tk GUI toolkit shipped with python
- Specific Geometry Management methods
- Apply event triggers to the widgets
- Create Admin. & User Panel to Respond to user interactions.
- PDBC - Python Database Connection Configuration with SQL.
- MySQL Functions
- MySQL - Import and Export data
- Organizes Data Structure into Data Tables Services
- Manage & Manipulate Database
- Database operations -create/manage/ access & search
- Updates, join, clause
- Retrieve Data information.
- Introduction to SQL SERVER
- Storing and Retrieving Databases
- Sql Optimization and CRUD operations
- Data Import and Export from different sources
- SQL Operators, Join, Clause
- Deep Dive into SQL Functions
- Working with Subqueries, Views and Stored Procedure
- Linear Algebra Concepts.
- Matrix Operations Like Addition, Subtraction, Multiplication & Inversion
- Linear Models To Solve Real-world Problems
- Linear Regression to predict prices based on historical data.
- Linear Algebra For Data Science & How To Implement It With Python.
- NumPy - Numerical Python Techniques
- Single & Multi-Dimensional Arrays in NumPy
- NumPy Vectorization, Matrices, Indexing, and Slicing Concepts
- NumPy Mathematical, Arithmetic, Statistics Functions
- Random number generators in NumPy
- Linear Algebra & array computing
- Computing diff. Attributes of NumPy arrays.
- Use open source data analysis and manipulation tools
- Cleaning and Plotting data
- Working with data sets with Easy handling of missing data
- Flexible Merging, Joining, Reshaping and Pivoting of data sets
- Functions for analyzing, cleaning, exploring, and manipulating data.
- Powerful data structures for data analysis
- Time Series and Delta Attributes
- Slicing, Indexing & Sub- Setting of large data sets
- Time series-specific functionality
- Date Range Generation
- Frequency conversion
- Pandas Data Frame
- Understand Trends, And Patterns, And To Make Correlations.
- Create Plot By Connecting The Points Defined By X & Y Values.
- Create Publication Quality Plots.
- Matplotlib Utilities Under The “pyplot” Submodule
- Interactive Format Environments Across Platforms
- Customize Visual Style And Layout.
- Creating static, animated, and interactive visualizations.
- Generate A Wide Range Of Plots, Including Line Plots, Scatter Plots, Bar Plots, Histograms, Pie Charts, And More.
- Functions For Optimization, Stats And Signal Processing.
- Algorithms For Optimization, Integration, Interpolation Queries
- Algebraic Equations, Differential Equations, Statistics
- Extends Numpy Providing Additional Tools
- Array Computing And Provides Specialized Data Structures
- Numerical Integration
- Image Manipulation & Optimization
- Signal Processing & Fast Transforms
- Cloud-based service for software development and version control using Git, allowing developers to store and manage their code
- Storing, tracking, and collaborating on open-source projects
- Projects Include:
- Data Scrubbing/Cleaning
- Exploratory Data Analysis
- Interactive Data Visualization
- Cluster the Information in A Given Dataset
- Data Models To Wave Artificial Intelligence And Machine Learning.
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