Complete roadmap to learn data science in 2024 👇👇

1. Learn the Basics:
- Brush up on your mathematics, especially statistics.
- Familiarize yourself with programming languages like Python or R.
- Understand basic concepts in databases and data manipulation.

2. Programming Proficiency:
- Develop strong programming skills, particularly in Python or R.
- Learn data manipulation libraries (e.g., Pandas) and visualization tools (e.g., Matplotlib, Seaborn).

3. Statistics and Mathematics:
- Deepen your understanding of statistical concepts.
- Explore linear algebra and calculus, especially for machine learning.

4. Data Exploration and Preprocessing:
- Practice exploratory data analysis (EDA) techniques.
- Learn how to handle missing data and outliers.

5. Machine Learning Fundamentals:
- Understand basic machine learning algorithms (e.g., linear regression, decision trees).
- Learn how to evaluate model performance.

6. Advanced Machine Learning:
- Dive into more complex algorithms (e.g., SVM, neural networks).
- Explore ensemble methods and deep learning.

7. Big Data Technologies:
- Familiarize yourself with big data tools like Apache Hadoop and Spark.
- Learn distributed computing concepts.

8. Feature Engineering and Selection:
- Master techniques for creating and selecting relevant features in your data.

9. Model Deployment:
- Understand how to deploy machine learning models to production.
- Explore containerization and cloud services.

10. Version Control and Collaboration:
- Use version control systems like Git.
- Collaborate with others using platforms like GitHub.

11. Stay Updated:
- Keep up with the latest developments in data science and machine learning.
- Participate in online communities, read research papers, and attend conferences.

12. Build a Portfolio:
- Showcase your projects on platforms like GitHub.
- Develop a portfolio demonstrating your skills and expertise.


Resources for Projects
https://t.me/codingdidi

ENJOY LEARNING 👍👍

Complete Python topics required for the Data Engineer role:

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻:

- Python Syntax
- Data Types
- Lists
- Tuples
- Dictionaries
- Sets
- Variables
- Operators
- Control Structures:
- if-elif-else
- Loops
- Break & Continue try-except block
- Functions
- Modules & Packages

➤ 𝗣𝗮𝗻𝗱𝗮𝘀:

- What is Pandas & imports?
- Pandas Data Structures (Series, DataFrame, Index)
- Working with DataFrames:
-> Creating DFs
-> Accessing Data in DFs Filtering & Selecting Data
-> Adding & Removing Columns
-> Merging & Joining in DFs
-> Grouping and Aggregating Data
-> Pivot Tables

- Input/Output Operations with Pandas:
-> Reading & Writing CSV Files
-> Reading & Writing Excel Files
-> Reading & Writing SQL Databases
-> Reading & Writing JSON Files
-> Reading & Writing - Text & Binary Files

➤ 𝗡𝘂𝗺𝗽𝘆:

- What is NumPy & imports?
- NumPy Arrays
- NumPy Array Operations:
- Creating Arrays
- Accessing Array Elements
- Slicing & Indexing
- Reshaping, Combining & Arrays
- Arithmetic Operations
- Broadcasting
- Mathematical Functions
- Statistical Functions

➤ 𝗕𝗮𝘀𝗶𝗰𝘀 𝗼𝗳 𝗣𝘆𝘁𝗵𝗼𝗻, 𝗣𝗮𝗻𝗱𝗮𝘀, 𝗡𝘂𝗺𝗽𝘆 are more than enough for Data Engineer role.

Hiring for Computer Vision Professionals with the below skills

Required Skills :-
(i) 4 to 7 years of experience in Image processing and image analytics with computational capabilities, Scalable CV architecture with strong understanding of Image acquisition system (Camera, Lighting etc).
(ii) Tech Enthusiastic with signs of continuous learning of industry best practices in the domain of Image / Video processing.
(iii) Minimum 2 to 5 years of experience in C++ development.

Mandatory Skills :-
(i) C++
(ii) OpenCV
(iii) Keras, Tensorflow, Pytorch, Scikit-learn, Scikit-image

Interested candidates can share their profiles on the below email IDs,
omnishishankar.mishra@neilsoft.com
abhijeet.gupte@neilsoft.com
vaishnavi.rakhunde@neilsoft.com

How to enter into Data Science

👉Start with the basics: Learn programming languages like Python and R to master data analysis and machine learning techniques. Familiarize yourself with tools such as TensorFlow, sci-kit-learn, and Tableau to build a strong foundation.

👉Choose your target field: From healthcare to finance, marketing, and more, data scientists play a pivotal role in extracting valuable insights from data. You should choose which field you want to become a data scientist in and start learning more about it.

👉Build a portfolio: Start building small projects and add them to your portfolio. This will help you build credibility and showcase your skills.

𝐇𝐨𝐰 𝐭𝐨 𝐈𝐧𝐭𝐫𝐨𝐝𝐮𝐜𝐞 𝐘𝐨𝐮𝐫𝐬𝐞𝐥𝐟 𝐢𝐧 𝐚 𝐏𝐡𝐨𝐧𝐞 𝐈𝐧𝐭𝐞𝐫𝐯𝐢𝐞𝐰? [ Part-1]

𝐇𝐑: Hello, am I speaking with [Your Name]?

[Your Name]: Yes, this is [Your Name] speaking.

[Your Name]: May I know who is calling, please?

𝐇𝐑: Hi [Your Name], this is [HR's Name] from XYZ Company.

𝐇𝐑: I'm calling because you applied for the Data Analyst role at our company.

[Your Name]: Yes, that's correct. Thank you for reaching out.

𝐇𝐑: [Your Name], could you tell me a bit about yourself?

[Your Name]: Sure! I recently graduated with a bachelor's degree in [Your Degree] from [Your University]. During my studies, I developed a strong interest in data analytics, particularly in how data can drive decision-making and improve business outcomes.

In college, I took courses in statistics, data visualization, and programming, which gave me a solid foundation in data analytics concepts. I also completed an internship at [Internship Company], where I worked on [specific project or task], honing my skills in data analysis and gaining hands-on experience with tools like Excel, SQL, and Python.

Now, I'm eager to apply my knowledge and skills in a professional setting and contribute to XYZ Company's success. I'm particularly drawn to your company's innovative approach to [specific area related to the company's work] and believe that my background and enthusiasm for data analytics would make me a valuable addition to your team.

𝐇𝐑: That sounds great, [Your Name]! Thank you for sharing.

[Your Name]: Thank you for giving me the opportunity!


Like this post if you want me to continue this 👍❤️

Company: Gainwell Technologies!
Position: Data Analyst
Experience: Freshers/ Experienced

https://jobs.gainwelltechnologies.com/job/Bangalore-Data-Analyst-KA-560100/1150924900/

One day or Day one. You decide.

Data Science edition.

𝗢𝗻𝗲 𝗗𝗮𝘆 : I will learn SQL.
𝗗𝗮𝘆 𝗢𝗻𝗲: Download mySQL Workbench.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will build my projects for my portfolio.
𝗗𝗮𝘆 𝗢𝗻𝗲: Look on Kaggle for a dataset to work on.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will master statistics.
𝗗𝗮𝘆 𝗢𝗻𝗲: Start the free Khan Academy Statistics and Probability course.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will learn to tell stories with data.
𝗗𝗮𝘆 𝗢𝗻𝗲: Install Tableau Public and create my first chart.

𝗢𝗻𝗲 𝗗𝗮𝘆: I will become a Data Scientist.
𝗗𝗮𝘆 𝗢𝗻𝗲: Update my resume and apply to some Data Science job postings.

Here's the good news for you all .!

I'm starting two series on my Instagram channel from Wednesday 24th July.

1. SQL - reel will be at 11:00 am
2. Statistics -- reel will be posted at 6:30 pm


I need your support to make it ✅😍 more engaging.

Another good news is
From coming weekend

✓ Starting a logic building series on yt
✓ Along with the tableau series.

Let me know what you think or if there's something you wanna add on.

Swiss Re is hiring!
Position: Data Analyst
Qualification: Bachelor’s/ Master’s Degree
Salary: 8 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore, India

📌Apply Now: https://careers.swissre.com/job/Bangalore-Data-Analyst-KA/1050003301/


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All the best 👍 👍

Below is a list of companies that are offering internships in the United Kingdom:

SLB – Data Science Intern
Tencent – NLP Research Intern
Cohere – Research Intern
Viridien – ML Intern
Tencent – Data Product Intern
Watchfinder – Data Engineer Intern

Check for these companies on LinkedIn to apply for these roles.

-------------Day-1 SQL----------

SQL is a standard language for accessing and manipulating databases.

✅What is SQL?
- SQL stands for Structured Query Language
- SQL lets you access and manipulate databases
- SQL became a standard of the American National Standards Institute (ANSI) in 1986, and of the International Organization for Standardization (ISO) in 1987

✅ What Can SQL do?
- SQL can execute queries against a database
- SQL can retrieve data from a database
- SQL can insert records in a database
- SQL can update records in a database
- SQL can delete records from a database
- SQL can create new databases
- SQL can create new tables in a database
- SQL can create stored procedures in a database
- SQL can create views in a database
- SQL can set permissions on tables, procedures, and views

✅✅ What Is a SQL Dialect, and Which one Should You Learn?

-----MySQL-----

MySQL is often compared to a handy free tool you might find in a toolbox. Imagine having a gadget that doesn't come with a price tag, yet is loved and recommended by many. That's MySQL for the digital world.



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Happy learning 🎊!

-------- Day-1 Statistics ---

Day-1 Statistics

✅ What is statistics?

Statistics is the study of the collection, analysis, interpretation, presentation, and organization of data. In other words, it is a mathematical discipline to collect, summarize data. Also, we can say that statistics is a branch of applied mathematics.

✅ Why study statistics?
statistics is one of the most important things that you can study.

To be more specific, here are some claims that we have heard on several occasions. (We are not saying that each one of these claims is true!)

- 4 out of 5 dentists recommend Dentine.
- Almost 85% of lung cancers in men and 45% in women are tobacco-related.

- People tend to be more persuasive when they look others directly in the eye and speak loudly and quickly.
- Women make 75 cents to every dollar a man makes when they work the same job.


✅ Common terms in Statistics!


1. Population

A population is a selected individual or group representing the full set of members of a certain group of interest.

2. Sample

A sample is a subset drawn from a larger population. If this drawing is accomplished in such a manner that each member of the population has an equitable chance of selection, the result is referred to as a random sample.

3. Parameters

A parameter is a value which is generated from a population. If I had all the data of all humans on Earth and generated the mean age, this value would be a parameter.

4. Statistics 📈

A statistic is a value which is generated from a sample

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And don't forget to comment 😍

---------Day-2 of SQL-------------


✅ What is a Database?

- Definition: A database is an organized collection of structured information or data, typically stored electronically in a computer system.
- Purpose: Databases are used to store, manage, and retrieve data efficiently.

✅What is DBMS?

- Definition: DBMS (Database Management System) is software that interacts with end-users, applications, and the database itself to capture and analyze data.
- Function: It provides a systematic way to create, retrieve, update, and manage data.


✅ Features of DBMS

1. Data Integrity: Ensures accuracy and consistency of data.
2. Data Security: Protects data from unauthorized access.
3. Data Recovery: Provides mechanisms for data backup and recovery.
4. Data Independence: Separates data from application programs.
5. Concurrency Control: Manages simultaneous data access by multiple users.
6. Data Management: Facilitates data storage, retrieval, and update.

✅ How Can We Interact with DBMS?

- SQL (Structured Query Language): The most common language used to interact with a DBMS for querying, updating, and managing data.

- Database Interfaces: Tools and applications (e.g., phpMyAdmin, pgAdmin) that provide a graphical interface to interact with the database.

- APIs: Application Programming Interfaces that allow software applications to interact with the DBMS programmatically.

✅What is a Query and Why is it Used?

- Definition: A query is a request for data or information from a database table or combination of tables.
- Purpose: Used to extract specific data by filtering, sorting, and presenting it based on certain conditions.



Example of a Query on Amazon

Scenario

Extracting product details for all items in the "Electronics" category with ratings above 4 stars.
- SQL Example:

  SELECT product_name, price, rating 
  FROM products 
  WHERE category = 'Electronics' AND rating > 4;
  

- Usage: This query helps Amazon identify and display high-rated electronic products to customers, enhancing user experience and satisfaction.


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Those who are looking for the tutorials to learn tableau!!


Check out the video 📸 😍😍

https://yt.openinapp.co/l3963

📊 Day-2 of statistics 📈

Read caption and join telegram channel.
👉🏻👉🏻 Statistics have majorly categorised into two types:

✅ Descriptive statistics
✅ Inferential statistics

🎉Descriptive Statistics

In this type of statistics, the data is summarised through the given observations. The summarisation is one from a sample of population using parameters such as the mean or standard deviation.

Descriptive statistics is a way to organise, represent and describe a collection of data using tables, graphs, and summary measures. For example, the collection of people in a city using the internet or using Television.

🎉 Inferential Statistics

This type of statistics is used to interpret the meaning of Descriptive statistics. That means once the data has been collected, analysed and summarised then we use these stats to describe the meaning of the collected data. Or we can say, it is used to draw conclusions from the data that depends on random variations such as observational errors, sampling variation, etc.