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Data Analytics
Data analytics is the process of examining data to draw conclusions, make informed decisions, and drive business growth. It involves various techniques, tools, and methodologies to transform raw data into actionable insights.
Types of Data Analytics:
1. Descriptive Analytics (what happened)
2. Diagnostic Analytics (why it happened)
3. Predictive Analytics (what might happen)
4. Prescriptive Analytics (what should happen)
Data Analytics Applications:
1. Business Intelligence (BI)
2. Customer Relationship Management (CRM)
3. Market Research
4. Operations Optimization
5. Healthcare Analytics
6. Financial Analysis
7. Cybersecurity
Data Analytics Tools:
1. Excel
2. Tableau
3. Power BI
4. Python libraries (Pandas, NumPy, Matplotlib)
5. R programming language
6. SQL
7. Machine Learning frameworks (TensorFlow, PyTorch)
Data Analytics Techniques:
1. Data Mining
2. Statistical Modeling
3. Data Visualization
4. Regression Analysis
5. Clustering
6. Decision Trees
7. Neural Networks
Career Paths in Data Analytics:
1. Data Analyst
2. Data Scientist
3. Business Analyst
4. Data Engineer
5. Data Architect
6. Quantitative Analyst
7. Marketing Analyst
Skills Required:
1. Mathematical and statistical knowledge
2. Programming skills (Python, R, SQL)
3. Data visualization expertise
4. Communication and storytelling
5. Business acumen
6. Analytical thinking
7. Problem-solving
Would you like to explore:
1. A specific data analytics tool or technique?
2. Real-world applications or case studies?
3. Career development in data analytics?
4. Data science and machine learning?
Let me know, and I'll provide more insights.
Data analytics is the process of examining data to draw conclusions, make informed decisions, and drive business growth. It involves various techniques, tools, and methodologies to transform raw data into actionable insights.
Types of Data Analytics:
1. Descriptive Analytics (what happened)
2. Diagnostic Analytics (why it happened)
3. Predictive Analytics (what might happen)
4. Prescriptive Analytics (what should happen)
Data Analytics Applications:
1. Business Intelligence (BI)
2. Customer Relationship Management (CRM)
3. Market Research
4. Operations Optimization
5. Healthcare Analytics
6. Financial Analysis
7. Cybersecurity
Data Analytics Tools:
1. Excel
2. Tableau
3. Power BI
4. Python libraries (Pandas, NumPy, Matplotlib)
5. R programming language
6. SQL
7. Machine Learning frameworks (TensorFlow, PyTorch)
Data Analytics Techniques:
1. Data Mining
2. Statistical Modeling
3. Data Visualization
4. Regression Analysis
5. Clustering
6. Decision Trees
7. Neural Networks
Career Paths in Data Analytics:
1. Data Analyst
2. Data Scientist
3. Business Analyst
4. Data Engineer
5. Data Architect
6. Quantitative Analyst
7. Marketing Analyst
Skills Required:
1. Mathematical and statistical knowledge
2. Programming skills (Python, R, SQL)
3. Data visualization expertise
4. Communication and storytelling
5. Business acumen
6. Analytical thinking
7. Problem-solving
Would you like to explore:
1. A specific data analytics tool or technique?
2. Real-world applications or case studies?
3. Career development in data analytics?
4. Data science and machine learning?
Let me know, and I'll provide more insights.
Here's a suggested 4-day, 2-hours-per-day step-by-step plan for revising Python for data analysis:
Day 1: Python Basics and Data Structures (2 hours)
1. Review Python basics (30 minutes):
- Variables, data types, operators
- Control structures (if/else, for loops, while loops)
- Functions
2. Data Structures (45 minutes):
- Lists: indexing, slicing, methods (append, extend, sort)
- Tuples: creation, indexing, slicing
- Dictionaries: creation, key-value pairs, methods (get, items, keys)
- Sets: creation, operations (union, intersection, difference)
3. Practice exercises (45 minutes):
- LeetCode, HackerRank, or (link unavailable) exercises
Day 2: NumPy, Pandas, and Data Manipulation (2 hours)
1. NumPy (45 minutes):
- Arrays: creation, indexing, slicing, operations (basic math, aggregation)
- Array methods (reshape, transpose, concatenate)
2. Pandas (45 minutes):
- DataFrames: creation, indexing, selecting data
- Data manipulation: filtering, grouping, sorting
- Merging and joining DataFrames
3. Practice exercises (30 minutes):
- Pandas tutorials, DataCamp exercises
Day 3: Data Visualization and File Handling (2 hours)
1. Data Visualization (45 minutes):
- Matplotlib: plotting basics, customizing plots
- Seaborn: visualization library, plotting distributions
2. File Handling (45 minutes):
- Reading and writing CSV files (pandas)
- Reading and writing Excel files (pandas)
- Handling JSON files (json library)
3. Practice exercises (30 minutes):
- Visualization exercises, DataCamp projects
Day 4: Data Analysis and Machine Learning (2 hours)
1. Data Analysis (45 minutes):
- Descriptive statistics (mean, median, mode)
- Inferential statistics (hypothesis testing)
- Data correlation and regression
2. Machine Learning (45 minutes):
- Scikit-learn: introduction, basic algorithms (linear regression, decision trees)
- Model evaluation metrics (accuracy, precision, recall)
3. Practice exercises (30 minutes):
- Scikit-learn tutorials, Kaggle competitions
Additional Tips:
- Practice with real-world datasets (e.g., UCI Machine Learning Repository)
- Use Jupyter Notebooks or Google Colab for interactive coding
- Review documentation and official tutorials for each library
- Join online communities (e.g., Reddit's r/learnpython, r/dataanalysis) for support
Resources:
- w3school
- DataCamp
- Kaggle
- UCI Machine Learning Repository
Stay focused, and you'll be well-prepared for data analysis with Python!
Would you like me to suggest specific exercises or projects?
Join WhatsApp Channel - https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h
Day 1: Python Basics and Data Structures (2 hours)
1. Review Python basics (30 minutes):
- Variables, data types, operators
- Control structures (if/else, for loops, while loops)
- Functions
2. Data Structures (45 minutes):
- Lists: indexing, slicing, methods (append, extend, sort)
- Tuples: creation, indexing, slicing
- Dictionaries: creation, key-value pairs, methods (get, items, keys)
- Sets: creation, operations (union, intersection, difference)
3. Practice exercises (45 minutes):
- LeetCode, HackerRank, or (link unavailable) exercises
Day 2: NumPy, Pandas, and Data Manipulation (2 hours)
1. NumPy (45 minutes):
- Arrays: creation, indexing, slicing, operations (basic math, aggregation)
- Array methods (reshape, transpose, concatenate)
2. Pandas (45 minutes):
- DataFrames: creation, indexing, selecting data
- Data manipulation: filtering, grouping, sorting
- Merging and joining DataFrames
3. Practice exercises (30 minutes):
- Pandas tutorials, DataCamp exercises
Day 3: Data Visualization and File Handling (2 hours)
1. Data Visualization (45 minutes):
- Matplotlib: plotting basics, customizing plots
- Seaborn: visualization library, plotting distributions
2. File Handling (45 minutes):
- Reading and writing CSV files (pandas)
- Reading and writing Excel files (pandas)
- Handling JSON files (json library)
3. Practice exercises (30 minutes):
- Visualization exercises, DataCamp projects
Day 4: Data Analysis and Machine Learning (2 hours)
1. Data Analysis (45 minutes):
- Descriptive statistics (mean, median, mode)
- Inferential statistics (hypothesis testing)
- Data correlation and regression
2. Machine Learning (45 minutes):
- Scikit-learn: introduction, basic algorithms (linear regression, decision trees)
- Model evaluation metrics (accuracy, precision, recall)
3. Practice exercises (30 minutes):
- Scikit-learn tutorials, Kaggle competitions
Additional Tips:
- Practice with real-world datasets (e.g., UCI Machine Learning Repository)
- Use Jupyter Notebooks or Google Colab for interactive coding
- Review documentation and official tutorials for each library
- Join online communities (e.g., Reddit's r/learnpython, r/dataanalysis) for support
Resources:
- w3school
- DataCamp
- Kaggle
- UCI Machine Learning Repository
Stay focused, and you'll be well-prepared for data analysis with Python!
Would you like me to suggest specific exercises or projects?
Join WhatsApp Channel - https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h
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Data Analyst Roadmap.pdf
904.4 KB
Data Analyst Roadmapπ₯π₯π₯
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Job Role: System Engineer β Freshers
Location: Gurugram
Experience: Freshers
Salary: Upto βΉ6 LPA
βοΈ Apply Link:
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πShare This opportunity with your Friends
IBM Hiring Fresher Associate Systems Software Engineer
Job Designation : Associate Systems Engineer
Salary : 5 LPA β 8 LPA
Qualification : Bachelorβs Degree
Experience : Freshers
Skill Set :
- Programming (preferably in Java, Python, Node.js) & Software Development Life Cycle Concepts
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Job Designation : Associate Systems Engineer
Salary : 5 LPA β 8 LPA
Qualification : Bachelorβs Degree
Experience : Freshers
Skill Set :
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Skill Set :
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Job Designation : Software Engineer Backend Developer
Salary : 8 LPA β 12 LPA
Qualification : Bachelorβs degree
Experience : Freshers / 0 β 2 years
Skill Set :
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- Proficient in Relational Databases such as MySQL.
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Complete JavaScript Road Mapπ₯
A-Z JavaScriptπ
1.Variables
β³ var
β³ let
β³ const
2. Data Types
β³ number
β³ string
β³ boolean
β³ null
β³ undefined
β³ symbol
3.Declaring variables
β³ var
β³ let
β³ const
4.Expressions
Primary expressions
β³ this
β³ Literals
β³ []
β³ {}
β³ function
β³ class
β³ function*
β³ async function
β³ async function*
β³ /ab+c/i
β³ string
β³ ( )
Left-hand-side expressions
β³ Property accessors
β³ ?.
β³ new
β³ new .target
β³ import.meta
β³ super
β³ import()
5.operators
β³ Arithmetic Operators: +, -, *, /, %
β³ Comparison Operators: ==, ===, !=, !==, <, >, <=, >=
β³ Logical Operators: &&, ||, !
6.Control Structures
β³ if
β³ else if
β³ else
β³ switch
β³ case
β³ default
7.Iterations/Loop
β³ do...while
β³ for
β³ for...in
β³ for...of
β³ for await...of
β³ while
8.Functions
β³ Arrow Functions
β³ Default parameters
β³ Rest parameters
β³ arguments
β³ Method definitions
β³ getter
β³ setter
9.Objects and Arrays
β³ Object Literal: { key: value }
β³ Array Literal: [element1, element2, ...]
β³ Object Methods and Properties
β³ Array Methods: push(), pop(), shift(), unshift(),
splice(), slice(), forEach(), map(), filter()
10.Classes and Prototypes
β³ Class Declaration
β³ Constructor Functions
β³ Prototypal Inheritance
β³ extends keyword
β³ super keyword
β³ Private class features
β³ Public class fields
β³ static
β³ Static initialization blocks
11.Error Handling
β³ try,
β³ catch,
β³ finally (exception handling)
ADVANCED CONCEPTS
--------------------------
12.Closures
β³ Lexical Scope
β³ Function Scope
β³ Closure Use Cases
13.Asynchronous JavaScript
β³ Callback Functions
β³ Promises
β³ async/await Syntax
β³ Fetch API
β³ XMLHttpRequest
14.Modules
β³ import and export Statements (ES6 Modules)
β³ CommonJS Modules (require, module.exports)
15.Event Handling
β³ Event Listeners
β³ Event Object
β³ Bubbling and Capturing
16.DOM Manipulation
β³ Selecting DOM Elements
β³ Modifying Element Properties
β³ Creating and Appending Elements
17.Regular Expressions
β³ Pattern Matching
β³ RegExp Methods: test(), exec(), match(), replace()
18.Browser APIs
β³ localStorage and sessionStorage
β³ navigator Object
β³ Geolocation API
β³ Canvas API
19.Web APIs
β³ setTimeout(), setInterval()
β³ XMLHttpRequest
β³ Fetch API
β³ WebSockets
20.Functional Programming
β³ Higher-Order Functions
β³ map(), reduce(), filter()
β³ Pure Functions and Immutability
21.Promises and Asynchronous Patterns
β³ Promise Chaining
β³ Error Handling with Promises
β³ Async/Await
22.ES6+ Features
β³ Template Literals
β³ Destructuring Assignment
β³ Rest and Spread Operators
β³ Arrow Functions
β³ Classes and Inheritance
β³ Default Parameters
β³ let, const Block Scoping
23.Browser Object Model (BOM)
β³ window Object
β³ history Object
β³ location Object
β³ navigator Object
24.Node.js Specific Concepts
β³ require()
β³ Node.js Modules (module.exports)
β³ File System Module (fs)
β³ npm (Node Package Manager)
25.Testing Frameworks
β³ Jasmine
β³ Mocha
β³ Jest
------------------- END-------------------
Some Good Resources To Learn JavaScript
1.Documentation
Mozilla MDN Web Docs
developer.mozilla.org/en-US/docs/Webβ¦
DevDocs
devdocs.io/javascript/
2. Useful Channel's
Javascript Courses: https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h
Programming Resources: https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h
FreeCodeCamp: youtube.com/c/FreeCodeCamp
Hope it helps ππ±
A-Z JavaScriptπ
1.Variables
β³ var
β³ let
β³ const
2. Data Types
β³ number
β³ string
β³ boolean
β³ null
β³ undefined
β³ symbol
3.Declaring variables
β³ var
β³ let
β³ const
4.Expressions
Primary expressions
β³ this
β³ Literals
β³ []
β³ {}
β³ function
β³ class
β³ function*
β³ async function
β³ async function*
β³ /ab+c/i
β³ string
β³ ( )
Left-hand-side expressions
β³ Property accessors
β³ ?.
β³ new
β³ new .target
β³ import.meta
β³ super
β³ import()
5.operators
β³ Arithmetic Operators: +, -, *, /, %
β³ Comparison Operators: ==, ===, !=, !==, <, >, <=, >=
β³ Logical Operators: &&, ||, !
6.Control Structures
β³ if
β³ else if
β³ else
β³ switch
β³ case
β³ default
7.Iterations/Loop
β³ do...while
β³ for
β³ for...in
β³ for...of
β³ for await...of
β³ while
8.Functions
β³ Arrow Functions
β³ Default parameters
β³ Rest parameters
β³ arguments
β³ Method definitions
β³ getter
β³ setter
9.Objects and Arrays
β³ Object Literal: { key: value }
β³ Array Literal: [element1, element2, ...]
β³ Object Methods and Properties
β³ Array Methods: push(), pop(), shift(), unshift(),
splice(), slice(), forEach(), map(), filter()
10.Classes and Prototypes
β³ Class Declaration
β³ Constructor Functions
β³ Prototypal Inheritance
β³ extends keyword
β³ super keyword
β³ Private class features
β³ Public class fields
β³ static
β³ Static initialization blocks
11.Error Handling
β³ try,
β³ catch,
β³ finally (exception handling)
ADVANCED CONCEPTS
--------------------------
12.Closures
β³ Lexical Scope
β³ Function Scope
β³ Closure Use Cases
13.Asynchronous JavaScript
β³ Callback Functions
β³ Promises
β³ async/await Syntax
β³ Fetch API
β³ XMLHttpRequest
14.Modules
β³ import and export Statements (ES6 Modules)
β³ CommonJS Modules (require, module.exports)
15.Event Handling
β³ Event Listeners
β³ Event Object
β³ Bubbling and Capturing
16.DOM Manipulation
β³ Selecting DOM Elements
β³ Modifying Element Properties
β³ Creating and Appending Elements
17.Regular Expressions
β³ Pattern Matching
β³ RegExp Methods: test(), exec(), match(), replace()
18.Browser APIs
β³ localStorage and sessionStorage
β³ navigator Object
β³ Geolocation API
β³ Canvas API
19.Web APIs
β³ setTimeout(), setInterval()
β³ XMLHttpRequest
β³ Fetch API
β³ WebSockets
20.Functional Programming
β³ Higher-Order Functions
β³ map(), reduce(), filter()
β³ Pure Functions and Immutability
21.Promises and Asynchronous Patterns
β³ Promise Chaining
β³ Error Handling with Promises
β³ Async/Await
22.ES6+ Features
β³ Template Literals
β³ Destructuring Assignment
β³ Rest and Spread Operators
β³ Arrow Functions
β³ Classes and Inheritance
β³ Default Parameters
β³ let, const Block Scoping
23.Browser Object Model (BOM)
β³ window Object
β³ history Object
β³ location Object
β³ navigator Object
24.Node.js Specific Concepts
β³ require()
β³ Node.js Modules (module.exports)
β³ File System Module (fs)
β³ npm (Node Package Manager)
25.Testing Frameworks
β³ Jasmine
β³ Mocha
β³ Jest
------------------- END-------------------
Some Good Resources To Learn JavaScript
1.Documentation
Mozilla MDN Web Docs
developer.mozilla.org/en-US/docs/Webβ¦
DevDocs
devdocs.io/javascript/
2. Useful Channel's
Javascript Courses: https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h
Programming Resources: https://whatsapp.com/channel/0029VaAVyoAEawe0EEAA4d1h
FreeCodeCamp: youtube.com/c/FreeCodeCamp
Hope it helps ππ±
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Qualification : Bachelorβs Degree
Experience : Freshers
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- Excellent programming skills in Java and Python.
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- Back-End Web Development proficiency
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- Good knowledge of software development life cycle.
Apply Here : https://techcompreviews.in/wipro-hiring-software-engineer/
Job Designation : Software Engineer
Qualification : Bachelorβs Degree
Experience : Freshers
Skill Set :
- Excellent programming skills in Java and Python.
- Solid computer Science and Software Development skills
- Back-End Web Development proficiency
- Programming and Object-Oriented Programming (OOP) knowledge
- Good knowledge of software development life cycle.
Apply Here : https://techcompreviews.in/wipro-hiring-software-engineer/
Genpact Freshers Software Consultant β Java
Job Designation : Software Consultant, Java Developer
Qualification : BE/BTech/MCA
Experience : Freshers / Experienced
Skill Set :
- Proficient in Java 8 programming.
- Expertise in working with data-bases β NoSQL and/or Relational.
- Familiar with design patterns, domain driven design and other software development best practices .
- Good knowledge of any cloud platform.
- General knowledge of working with complex business processes, data flows and consuming large amounts of data.
Apply Here : https://techcompreviews.in/genpact-hiring-java-developer/
Job Designation : Software Consultant, Java Developer
Qualification : BE/BTech/MCA
Experience : Freshers / Experienced
Skill Set :
- Proficient in Java 8 programming.
- Expertise in working with data-bases β NoSQL and/or Relational.
- Familiar with design patterns, domain driven design and other software development best practices .
- Good knowledge of any cloud platform.
- General knowledge of working with complex business processes, data flows and consuming large amounts of data.
Apply Here : https://techcompreviews.in/genpact-hiring-java-developer/
Coinbase Hiring Freshers Software Engineer
Job Designation : Software Engineer
Salary : 34. 8 LPA
Qualification : Bachelors or Masters degree
Experience : Freshers / Experienced
Skill Set :
- Good knowledge of programming/modeling, preferably in Golang, Ruby, JavaScript and Python.
- Sound knowledge of Software Development Life Cycle ( SDLC ) and testing skills.
- Good knowledge of Blockchain, crypto and web3 concepts.
- Familiar with AWS, Docker, Postgres, Ruby, MongoDB, React Native.
- Good knowledge of Operating systems iOS, Android.
Apply Here : https://techcompreviews.in/coinbase-hiring-software-engineer/
Job Designation : Software Engineer
Salary : 34. 8 LPA
Qualification : Bachelors or Masters degree
Experience : Freshers / Experienced
Skill Set :
- Good knowledge of programming/modeling, preferably in Golang, Ruby, JavaScript and Python.
- Sound knowledge of Software Development Life Cycle ( SDLC ) and testing skills.
- Good knowledge of Blockchain, crypto and web3 concepts.
- Familiar with AWS, Docker, Postgres, Ruby, MongoDB, React Native.
- Good knowledge of Operating systems iOS, Android.
Apply Here : https://techcompreviews.in/coinbase-hiring-software-engineer/
JPMorgan Chase Hiring Fresher Software Engineer
Job Designation : Software Engineer β Database
Qualification : Bachelorβs / Masters Degree
Experience : Freshers / Experienced
Skill Set :
- Hands-on practical experience in Tandem development in high availability environment
- Experience in Tandem INSPECT/e-INSPECT/visual inspect, Tandem COBOL, TACL
- Experience with Tandem PATHWAY, payment and debit networks (Visa, MC, Discover, Star, Pulse, etc)
- Demonstrates strong communication skills and the ability to interact with individuals at all levels
- Demonstrates strong problem analysis and resolution skills
- Strong problem analysis & resolution skills
- Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security
Apply Here : https://techcompreviews.in/jpmorgan-hiring-software-engineer/
Job Designation : Software Engineer β Database
Qualification : Bachelorβs / Masters Degree
Experience : Freshers / Experienced
Skill Set :
- Hands-on practical experience in Tandem development in high availability environment
- Experience in Tandem INSPECT/e-INSPECT/visual inspect, Tandem COBOL, TACL
- Experience with Tandem PATHWAY, payment and debit networks (Visa, MC, Discover, Star, Pulse, etc)
- Demonstrates strong communication skills and the ability to interact with individuals at all levels
- Demonstrates strong problem analysis and resolution skills
- Strong problem analysis & resolution skills
- Exposure to agile methodologies such as CI/CD, Application Resiliency, and Security
Apply Here : https://techcompreviews.in/jpmorgan-hiring-software-engineer/