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پستهای کانال
1. *Data Science Internship*
📍 _Chirpy AI | New Zealand_
_(Work from Home)_
_Apply here:_👇
https://bit.ly/44PCZEd
| 2 | FREE courses to learn Data Science in 2023
—————————————————————-
Python [ Harvard ]: https://t.co/k736uzifHP
SQL [ Stanford ]: https://t.co/0McNhq41HU
R [ IBM ]: https://t.co/w1KvTXZfYy
PowerBI [ Microsoft ]: https://t.co/1KTVs0xUxx
Mathematics [ MIT ]: https://t.co/WanP7fdDB0
Tableau: https://t.co/edNxTVBG48
Excel, PowerBI: https://t.co/QGnJG3vcwv
Probability: https://t.co/jmoONuWKVO
Statistics: https://t.co/Z328Xdjs2O
Linear Algebra: https://t.co/WSeySjWv7D
Machine Learning [ Google ]: https://t.co/5Qa2PdX674
Deep Learning: https://t.co/uexo9Vhvi0
Data Analysis: https://t.co/m0PqmBc838
Data Visualization: https://t.co/DkknCBQUMv | 1 864 |
| 3 | 🔸 Here are the release dates of some of the Programming Languages 🔸
#Get_to_know
⧩ Assembly - 1949
⧩ C - 1972
⧩ C++ - 1985
⧩ Python - 1991
⧩ R - 1993
⧩ Java - 1995
⧩ JavaScript - 1995
⧩ PHP - 1995
⧩ C# - 2000
⧩ Go - 2009
⧩ Kotlin - 2011
⧩ Julia - 2012 | 1 422 |
| 4 | Probability: http://mygreatlearning.com/academy/learn-for-free/courses/probability-for-data-science
Statistics: http://learn.saylor.org/course/view.php?id=28 | 2 045 |
| 5 | 🚀 _*Boston Consulting Group (BCG)* is hiring *Data Scientist* INTERNS_ 💯 👨💻👩💻
📌 APPLY here:👇👇
https://bit.ly/43mbDFg
https://bit.ly/43mbDFg | 2 160 |
| 6 | FREE certification course from Google to try in 2023
1. 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗟𝗲𝗮𝗿𝗻𝗶𝗻𝗴 𝗖𝗿𝗮𝘀𝗵 𝗖𝗼𝘂𝗿𝘀𝗲
https://lnkd.in/dbkXEGSY
2. 𝗗𝗮𝘁𝗮 𝗦𝗰𝗶𝗲𝗻𝗰𝗲 𝘄𝗶𝘁𝗵 𝗣𝘆𝘁𝗵𝗼𝗻
https://lnkd.in/dhFdCNnc
3. 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗖𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴 𝗙𝗼𝘂𝗻𝗱𝗮𝘁𝗶𝗼𝗻𝘀
https://lnkd.in/d_D6Jjai
4. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗚𝗼𝗼𝗴𝗹𝗲 𝗖𝗹𝗼𝘂𝗱 𝗘𝘀𝘀𝗲𝗻𝘁𝗶𝗮𝗹𝘀
https://lnkd.in/dN5mrNt9
5. 𝗜𝗻𝘁𝗿𝗼𝗱𝘂𝗰𝘁𝗶𝗼𝗻 𝘁𝗼 𝗕𝗮𝘀𝗲𝗹𝗶𝗻𝗲: 𝗗𝗮𝘁𝗮, 𝗠𝗟, 𝗔𝗜
https://lnkd.in/ddyJM5WR | 2 338 |
| 7 | Here is a roadmap for becoming a data analyst:
Learn Fundamentals:
Gain a strong foundation in mathematics and statistics.
Develop proficiency in a programming language like Python or R.
Familiarize yourself with data manipulation and analysis libraries such as pandas and NumPy.
Understand Data Analysis Concepts:
Learn about exploratory data analysis (EDA) techniques.
Study data visualization principles using tools like Matplotlib or Tableau.
Become familiar with statistical concepts and hypothesis testing.
Master SQL:
Learn Structured Query Language (SQL) for data querying and manipulation.
Understand database management systems and relational database concepts.
Gain Domain Knowledge:
Specialize in a specific industry or domain to understand its data requirements.
Learn about the relevant metrics and key performance indicators (KPIs) in that domain.
Develop Data Cleaning and Preprocessing Skills:
Learn techniques to handle missing data, outliers, and data inconsistencies.
Gain experience in data preprocessing tasks such as data transformation and feature engineering.
Learn Data Analysis Techniques:
Study various statistical analysis methods and models.
Explore predictive modeling techniques, such as regression and classification algorithms.
Understand time series analysis and forecasting.
Master Data Visualization:
Learn advanced data visualization techniques to effectively communicate insights.
Utilize tools like Tableau, Power BI, or matplotlib for creating impactful visualizations.
Acquire Business Intelligence Skills:
Understand the basics of business intelligence tools and dashboards.
Learn to create interactive dashboards for data reporting and analysis.
Gain Practical Experience:
Apply your skills through internships, projects, or Kaggle competitions.
Work on real-world datasets to gain hands-on experience in data analysis.
Continuously Learn and Stay Updated:
Keep up with the latest trends and advancements in data analysis and analytics tools.
Participate in online courses, workshops, and webinars to enhance your skills.
Remember, the roadmap may vary depending on individual preferences and career goals. It is important to adapt and continuously learn as the field of data analysis evolves. | 1 818 |
| 8 | Probabilistic Methods
ocw.mit.edu/courses/18-226-probabilistic-method-in-combinatorics-fall-2020/
Fourier Analysis
ocw.mit.edu/courses/18-103-fourier-analysis-fall-2013/
Intro to Statistics
coursera.org/learn/stanford-statistics
Calculus
learn.saylor.org/course/view.php?id=25
Statistics
learn.saylor.org/course/view.php?id=28 | 1 256 |
| 9 | 💙JOB OPENING UPDATE💙
Company: TechBridg
Role: Data Analyst
Exp: 0-1 Year
Apply Now:
https://hirect.in/webhirect/?type=1&p=ODQ3ODgyMjM4OTU0NzI1Mzc2&t=1689145001628&name=TechBridge%20consultancy%20Services&category=Data%20Analyst&uid=ZTNiOTVjYTE1OTIyNDY5YzhlZTMyMmM5NzljNg==
Company: DC
Role: Data Engineer
Exp: 1-3 years
Apply Now:
https://hirect.in/webhirect/?type=1&p=NzEwOTA5NzgyNDc5MDExODQw&t=1689144925548&name=DC&category=Data%20Engineer&uid=ZTNiOTVjYTE1OTIyNDY5YzhlZTMyMmM5NzljNg==
Company: Xerago
Role: Data Analyst
Exp: 0-1 Year
Apply Now
https://hirect.in/webhirect/?type=1&p=NzE0NjExNDU5MzE2OTI4NTEy&t=1689144718998&name=Xerago&category=Data%20Analyst&uid=ZTNiOTVjYTE1OTIyNDY5YzhlZTMyMmM5NzljNg==
Company: Quess IT
Role: Data scientist
Exp:2+ years
Apply Now:
https://www.linkedin.com/jobs/view/3657472604
Company: RheoAI
Role: Data Analyst
Exp: 0-1 Year
Apply Now:
https://hirect.in/webhirect/?type=1&p=NjkwNTg0Nzg4MjEzNTc1Njgw&t=1689144774989&name=RheoAI&category=Data%20Analyst&uid=ZTNiOTVjYTE1OTIyNDY5YzhlZTMyMmM5NzljNg==
Company: EinNel
Role: Data Scientist
Exp: 0-2 years
Apply Now:
https://hirect.in/webhirect/?type=1&p=NjYzNTA1ODc1MDU0NTE0MTc2&t=1689144892598&name=EinNel&category=Data%20Scientist&uid=ZTNiOTVjYTE1OTIyNDY5YzhlZTMyMmM5NzljNg==
Company: Whitecrow Research
Role: Data scientist
Exp: 0-3 years
Apply Now: https://www.linkedin.com/jobs/view/3660514397
ALL THE BEST💙 | 1 264 |
| 10 | Best Paying IT Jobs in Technology
Let’s Check on the Top Best Paying IT Jobs in Technology for 2023.
Software Engineering Manager: $134,156
Mobile Applications Developer: $111,468
Information Systems Security Manager: $153,677
Database Manager: $58,161
Data Security Analyst: $71,226
Product Manager: $100,000
Artificial Intelligence (AI) Engineer: $110,000
Full-Stack Developer: $106,000
Cloud Architect: $107,000
DevOps Engineer: $140,000
Blockchain Engineer: $150,000
Software Architect: $114,000
Big Data Engineer: $140,000
Internet of Things (IoT) Solutions Architect: $130,000
Data Scientist: $150,000.
Cyber Security Engineer: $92,000
IT Systems Security Manager: $90,000
Applications Architect: $115,000
Data Architect: $116,920
Site Reliability Engineer: $80,000 to $200,000
Development Operations Manager: $75,000 to $125,000
IT Security Specialist: $50,000 to $120,000
Application Analyst: $53,000 to $83,000
User Interface Designer: $80,000 to $130,000
Application Developer: $85,000
Business Intelligence Analyst: $78,419
Software Test Engineer: $60,000-$80,000
Information Technology Manager: $85,000
User Experience Designer: $50,000 to $85,000
Business Intelligence Developer: $98,415
Hardware Design Engineer: $85,000
Solutions Engineer: $75,000
Network Security Engineer: $1,20,000
Data Warehouse Architect: $1,20,000
Cloud Engineer: $107,000
Enterprise Architect: $1,39,700
Computer Programmer: $75,000
Computer Systems Analyst: $65,000 to $85,000
Network and Computer System Administrators: $84,810 | 1 244 |
| 11 | 10 Websites to Find Remote Jobs:
👉 flexjobs.com
👉 wellfound.com
👉 workew.com
👉 justremote.co
👉 dynamitejobs.com
👉 remotive.com
👉 remote.co
👉 talent.hubstaff.com
👉 himalayas.app
👉 weworkremotely.com | 1 253 |
| 12 | What Are the Data Types Supported in Tableau?
Following data types are supported in Tableau:
Text (string) values
Date values
Date and time values
Numerical values
Boolean values (relational only)
Geographical values (used with maps) | 1 412 |
| 13 | Coursera: "SQL for Data Science" - Learn SQL Basics for Data Science Specialization: https://www.coursera.org/specializations/learn-sql-basics-data-science
Book: "SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis" by Renee M.P. Teate (Available on Amazon)
Companion Website: "SQL for Data Scientists" - Interactive SQL Editor:https://sqlfordatascientists.com/
Wiley: "SQL for Data Scientists: A Beginner's Guide for Building Datasets for Analysis" by Renee M.P. Teate
edX: "SQL for Data Science" - Relational database concepts and foundational SQL knowledge: https://www.edx.org/course/sql-for-data-science?index=product&queryID=d86ed550f6bd4e26d89d318c006223f0&position=3&results_level=second-level-results&search_index=product&term=Relational+database+concepts+and+foundational+SQL+knowledge%3A&campaign=SQL+for+Data+Science&source=edX&product_category=course&placement_url=https%3A%2F%2Fwww.edx.org%2Fsearch | 1 761 |
| 14 | SQL is a powerful tool for data scientists to extract and manipulate data from databases.
Here are some of the most common SQL commands used in data science:
🔰SELECT: This command is used to retrieve data from a database table. It allows you to specify which you want to retrieve and apply filters to the data.
🔰FROM: This command is used to specify the table from which you want to retrieve data.
WHERE: This command is used to filter data based on specific conditions.
🔰JOIN: This command is used to combine data from two or more tables based on a common column.
🔰GROUP BY: This command is used to group data based on one or more columns.
🔰ORDER BY: This command is used to sort data in ascending or descending order based on one or more columns.
🔰LIMIT: This command is used to limit the number of rows returned by a query.
🔰COUNT: This command is used to count the number of rows that meet a specific condition.
🔰AVG: This command is used to calculate the average value of a column.
🔰SUM: This command is used to calculate the sum of a column.
These commands are just a few examples of the many SQL commands that data scientists use to extract and manipulate data from databases. | 1 527 |
| 15 | Date: 02-03-2023
Company name: Biocon
Role: ML Engineer
Topic: nodes in DT, ensemble learning, adaboost, hierarchical clustering
1. List down the different types of nodes in Decision Trees.
The Decision Tree consists of the following different types of nodes:
1. Root node: It is the top-most node of the Tree from where the Tree starts.
2. Decision nodes: One or more Decision nodes that result in the splitting of data into multiple data segments and our main goal is to have the children nodes with maximum homogeneity or purity.
3. Leaf nodes: These nodes represent the data section having the highest homogeneity.
2. What is the Alpha Term in AdaBoost? How does it work?
In AdaBoost, multiple weak learners are trained to get the strong learner. As a result, hence calculating the error term of every weak learner is essential to know which weak learner is performing best and which is not.
The term Alpha is a parameter that indicates the weight that should be given to a particular weak learner algorithm. If the value of the term Alpha for a particular algorithm is high, that indicates that the model is performing best and the error rate for the same is low.
3. Since Ensemble Learning provides better output most of the time, why do you not use it all the time?
Although it provides a better outcome many times, it is not true that it will always perform better. There are several ensemble methods, each with its own advantages/disadvantages, and choosing one to use depends on the problem at hand. If there are models with high variance, then it will benefit from bagging. If the model is biased, it is better to use boosting. If the work is in probabilistic setting, the ensemble methods may not work because it is known that boosting delivers poor probability estimates.
4. What are the various types of Hierarchical Clustering?
The two different types of Hierarchical Clustering technique are as follows:
Agglomerative: It is a bottom-up approach, in which the algorithm starts with taking all data points as single clusters and merging them until one cluster is left.
Divisive: It is just the opposite of the agglomerative algorithm as it is a top-down approach. | 2 958 |
| 16 |  This for newbies those people can start through this course
https://simpli-web.app.link/e/WZmrj2xe9wb | 2 809 |
| 17 | Absolutely beginner firendly you can get overview on big day by join this course
https://simpli-web.app.link/e/1Nf3gZne9wb | 2 471 |
| 18 | Those you have basic knowledge in python they can enroll it
https://simpli-web.app.link/e/1Nf3gZne9wb | 2 041 |
| 19 | https://simpli-web.app.link/e/YzWqZU9d9wb | 1 792 |
| 20 | Date: 02-02-2023
Company name: Cognizant
Role: SQL Developer
Topic: superkey, buffer pool value, candidate key, null values
1. What are Query and Query language?
A query is nothing but a request sent to a database to retrieve data or information. The required data can be retrieved from a table or many tables in the database.
Query languages use various types of queries to retrieve data from databases. SQL, Datalog, and AQL are a few examples of query languages; however, SQL is known to be the widely used query language.
2. What are Superkey and candidate key?
A super key may be a single or a combination of keys that help to identify a record in a table. Know that Super keys can have one or more attributes, even though all the attributes are not necessary to identify the records.
A candidate key is the subset of Superkey, which can have one or more than one attributes to identify records in a table. Unlike Superkey, all the attributes of the candidate key must be helpful to identify the records.
3. What do you mean by buffer pool and mention its benefits?
A buffer pool in SQL is also known as a buffer cache. All the resources can store their cached data pages in a buffer pool. The size of the buffer pool can be defined during the configuration of an instance of SQL Server.
The following are the benefits of a buffer pool:
Increase in I/O performance
Reduction in I/O latency
Increase in transaction throughput
Increase in reading performance
4. What is the difference between Zero and NULL values in SQL?
When a field in a column doesn’t have any value, it is said to be having a NULL value. Simply put, NULL is the blank field in a table. It can be considered as an unassigned, unknown, or unavailable value. On the contrary, zero is a number, and it is an available, assigned, and known value.
If you are new to this channel then don't forget to Join and get regular Important Updates related to Data Science & Analytics Domain. | 1 709 |
