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Free learning Resources For Data Analysts, Data science, ML, AI, GEN AI and Job updates, career growth, Tech updates
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COMC is hiring for Data Analyst

Salary Range
$80,000.00 - $100,000.00

Apply Link: https://www.paycomonline.net/v4/ats/web.php/jobs/ViewJobDetails?job=100986&clientkey=DB4B2E90705AD5F0635FBD274EAB4268

Job Qualifications

Bachelorโ€™s degree in Computer Science, Information Systems, Statistics, or a related field.
2+ years of experience in data analysis.
Proficient in SQL for data querying and manipulation.
Experience with Power BI or similar data visualization tools.
Understanding of data warehousing and database structures.
Basic knowledge of statistical analysis and data modeling.
Excellent analytical and problem-solving skills.
Ability to communicate complex data insights in a clear and concise manner.

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All the best ๐Ÿ‘๐Ÿ‘
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Sharpsell.ai is hiring Business Analyst interns with 0-2 years of experience with a background of Computer Science/Information Systems/ Data Analytics, or a related field.

Candidates with knowledge of SQL and experience writing queries, familiar with BI tools such as Tableau/Power BI, or similar dashboarding platforms are preferred for the role.

This role is for Bangalore location, and three days working from office. Interested candidates please share your CVs with priyadarshini.r@sharpsell.ai
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That's all you need for a data analyst role and this is the kick starter for the data science role.

Begin and invest for your future.

Grad your seats Today!! ๐Ÿ˜๐Ÿ˜

๐Ÿ‘Œ Waiting to see you all in the session. โœ…
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Company: Sanofi!
Position: Trainee Data Analyst
Experienc๏ปฟe: Freshers (0 - 1 Years)
Location: Hyderabad, India

https://sanofi.wd3.myworkdayjobs.com/SanofiCareers/job/Hyderabad/Trainee-Data-Analyst_R2748187
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Company: HSBC!
Position: Business Analyst - Accounts
Qualifications: Bachelorโ€™s/ Masterโ€™s Degree
Salary: 7 - 12 LPA (Expected)
Experience: Freshers/ Experienced
Location: Work From Home/ Office

https://mycareer.hsbc.com/en_GB/external/PipelineDetail/Business-Analyst-Accounts-SSI-s/217665
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Adobe is hiring!
Position: Data Science Engineer
Qualifications: Bachelorโ€™s/ Masterโ€™s or PhD Degree
Salary: 8 - 23 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore, India

๐Ÿ“ŒApply Now: https://careers.adobe.com/us/en/job/ADOBUSR144292EXTERNALENUS/Data-Science-Engineer
Amazon is hiring!
Position: Data Analyst/ Business Analytics
Qualifications: Bachelorโ€™s/ Masterโ€™s Degree
Salary: 5 - 8 LPA (Expected)
Experience: Freshers/ Experienced
Location: Bangalore/ Hyderabad

๐Ÿ“ŒApply Now: https://www.amazon.jobs/en/jobs/2650214/data-analyst-in-easyship?

https://www.amazon.jobs/en/jobs/2574412/business-analyst-i-lat-mile-analytics-and-quality-lmaq?
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Gentle reminder ๐ŸŽ—๏ธ
"I've seen a great response from you guys and it truly means a lot ๐Ÿ™Œ๐Ÿป ๐Ÿ’ฏ because of your support and efforts, the maximum
number of seats are filled ๐Ÿ’บโœ…. *I want to inform all of you that the course enrollment will be closed on 20th June 2024 โš ๏ธโš ๏ธ. So, if you're still interested in enrolling in this course please update before 20th June* โ˜บ๏ธโญ
Thank you!!
Regards
Codingdidi
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Amazon Interview Process for Data Scientist position

๐Ÿ“Round 1- Phone Screen round
This was a preliminary round to check my capability, projects to coding, Stats, ML, etc.

After clearing this round the technical Interview rounds started. There were 5-6 rounds (Multiple rounds in one day).

๐Ÿ“ ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฎ- ๐——๐—ฎ๐˜๐—ฎ ๐—ฆ๐—ฐ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—•๐—ฟ๐—ฒ๐—ฎ๐—ฑ๐˜๐—ต:
In this round the interviewer tested my knowledge on different kinds of topics.

๐Ÿ“๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฏ- ๐——๐—ฒ๐—ฝ๐˜๐—ต ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ:
In this round the interviewers grilled deeper into 1-2 topics. I was asked questions around:
Standard ML tech, Linear Equation, Techniques, etc.

๐Ÿ“๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฐ- ๐—–๐—ผ๐—ฑ๐—ถ๐—ป๐—ด ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ-
This was a Python coding round, which I cleared successfully.

๐Ÿ“๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ ๐Ÿฑ- This was ๐—›๐—ถ๐—ฟ๐—ถ๐—ป๐—ด ๐— ๐—ฎ๐—ป๐—ฎ๐—ด๐—ฒ๐—ฟ where my fitment for the team got assessed.

๐Ÿ“๐—Ÿ๐—ฎ๐˜€๐˜ ๐—ฅ๐—ผ๐˜‚๐—ป๐—ฑ- ๐—•๐—ฎ๐—ฟ ๐—ฅ๐—ฎ๐—ถ๐˜€๐—ฒ๐—ฟ- Very important round, I was asked heavily around Leadership principles & Employee dignity questions.

So, here are my Tips if youโ€™re targeting any Data Science role:
-> Never make up stuff & donโ€™t lie in your Resume.
-> Projects thoroughly study.
-> Practice SQL, DSA, Coding problem on Leetcode/Hackerank.
-> Download data from Kaggle & build EDA (Data manipulation questions are asked)


Resources: https://topmate.io/codingdidi/digital_products

ENJOY LEARNING ๐Ÿ‘๐Ÿ‘
๐Ÿ‘10โค2๐Ÿ”ฅ1
๐’๐ญ๐ซ๐ข๐ง๐  ๐Œ๐š๐ง๐ข๐ฉ๐ฎ๐ฅ๐š๐ญ๐ข๐จ๐ง ๐ข๐ง ๐๐ฒ๐ญ๐ก๐จ๐ง:
Strings in Python are immutable sequences of characters.

๐Ÿ- ๐ฅ๐ž๐ง(): ๐‘๐ž๐ญ๐ฎ๐ซ๐ง๐ฌ ๐ญ๐ก๐ž ๐ฅ๐ž๐ง๐ ๐ญ๐ก ๐จ๐Ÿ ๐ญ๐ก๐ž ๐ฌ๐ญ๐ซ๐ข๐ง๐ .

my_string = "Hello"
length = len(my_string)  # length will be 5

๐Ÿ- ๐ฌ๐ญ๐ซ(): ๐‚๐จ๐ง๐ฏ๐ž๐ซ๐ญ๐ฌ ๐ง๐จ๐ง-๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐๐š๐ญ๐š ๐ญ๐ฒ๐ฉ๐ž๐ฌ ๐ข๐ง๐ญ๐จ ๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ.

num = 123
str_num = str(num)  # str_num will be "123"

๐Ÿ‘- ๐ฅ๐จ๐ฐ๐ž๐ซ() ๐š๐ง๐ ๐ฎ๐ฉ๐ฉ๐ž๐ซ(): ๐‚๐จ๐ง๐ฏ๐ž๐ซ๐ญ ๐š ๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ญ๐จ ๐ฅ๐จ๐ฐ๐ž๐ซ๐œ๐š๐ฌ๐ž ๐จ๐ซ ๐ฎ๐ฉ๐ฉ๐ž๐ซ๐œ๐š๐ฌ๐ž.

my_string = "Hello"
lower_case = my_string.lower()  # lower_case will be "hello"
upper_case = my_string.upper()  # upper_case will be "HELLO"

๐Ÿ’- ๐ฌ๐ญ๐ซ๐ข๐ฉ(): ๐‘๐ž๐ฆ๐จ๐ฏ๐ž๐ฌ ๐ฅ๐ž๐š๐๐ข๐ง๐  ๐š๐ง๐ ๐ญ๐ซ๐š๐ข๐ฅ๐ข๐ง๐  ๐ฐ๐ก๐ข๐ญ๐ž๐ฌ๐ฉ๐š๐œ๐ž ๐Ÿ๐ซ๐จ๐ฆ ๐š ๐ฌ๐ญ๐ซ๐ข๐ง๐ .

my_string = "   Hello   "
stripped_string = my_string.strip()  # stripped_string will be "Hello"

๐Ÿ“- ๐ฌ๐ฉ๐ฅ๐ข๐ญ(): ๐’๐ฉ๐ฅ๐ข๐ญ๐ฌ ๐š ๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ข๐ง๐ญ๐จ ๐š ๐ฅ๐ข๐ฌ๐ญ ๐จ๐Ÿ ๐ฌ๐ฎ๐›๐ฌ๐ญ๐ซ๐ข๐ง๐ ๐ฌ ๐›๐š๐ฌ๐ž๐ ๐จ๐ง ๐š ๐๐ž๐ฅ๐ข๐ฆ๐ข๐ญ๐ž๐ซ.

my_string = "apple,banana,orange"
fruits = my_string.split(",")  # fruits will be ["apple", "banana", "orange"]

๐Ÿ”- ๐ฃ๐จ๐ข๐ง(): ๐‰๐จ๐ข๐ง๐ฌ ๐ญ๐ก๐ž ๐ž๐ฅ๐ž๐ฆ๐ž๐ง๐ญ๐ฌ ๐จ๐Ÿ ๐š ๐ฅ๐ข๐ฌ๐ญ ๐ข๐ง๐ญ๐จ ๐š ๐ฌ๐ข๐ง๐ ๐ฅ๐ž ๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ฎ๐ฌ๐ข๐ง๐  ๐š ๐ฌ๐ฉ๐ž๐œ๐ข๐Ÿ๐ข๐ž๐ ๐ฌ๐ž๐ฉ๐š๐ซ๐š๐ญ๐จ๐ซ.

fruits = ["apple", "banana", "orange"]
my_string = ",".join(fruits)  # my_string will be "apple,banana,orange"

๐Ÿ•- ๐Ÿ๐ข๐ง๐() ๐š๐ง๐ ๐ข๐ง๐๐ž๐ฑ(): ๐’๐ž๐š๐ซ๐œ๐ก ๐Ÿ๐จ๐ซ ๐š ๐ฌ๐ฎ๐›๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ฐ๐ข๐ญ๐ก๐ข๐ง ๐š ๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐š๐ง๐ ๐ซ๐ž๐ญ๐ฎ๐ซ๐ง ๐ข๐ญ๐ฌ ๐ข๐ง๐๐ž๐ฑ.

my_string = "Hello, world!"
index1 = my_string.find("world")  # index1 will be 7
index2 = my_string.index("world")  # index2 will also be 7

๐Ÿ–- ๐ซ๐ž๐ฉ๐ฅ๐š๐œ๐ž(): ๐‘๐ž๐ฉ๐ฅ๐š๐œ๐ž๐ฌ ๐จ๐œ๐œ๐ฎ๐ซ๐ซ๐ž๐ง๐œ๐ž๐ฌ ๐จ๐Ÿ ๐š ๐ฌ๐ฎ๐›๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ฐ๐ข๐ญ๐ก ๐š๐ง๐จ๐ญ๐ก๐ž๐ซ ๐ฌ๐ฎ๐›๐ฌ๐ญ๐ซ๐ข๐ง๐ .

my_string = "Hello, world!"
new_string = my_string.replace("world", "Python")  # new_string will be "Hello, Python!"

๐Ÿ—- ๐ฌ๐ญ๐š๐ซ๐ญ๐ฌ๐ฐ๐ข๐ญ๐ก() ๐š๐ง๐ ๐ž๐ง๐๐ฌ๐ฐ๐ข๐ญ๐ก(): ๐‚๐ก๐ž๐œ๐ค๐ฌ ๐ข๐Ÿ ๐š ๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ฌ๐ญ๐š๐ซ๐ญ๐ฌ ๐จ๐ซ ๐ž๐ง๐๐ฌ ๐ฐ๐ข๐ญ๐ก ๐š ๐ฌ๐ฉ๐ž๐œ๐ข๐Ÿ๐ข๐ž๐ ๐ฌ๐ฎ๐›๐ฌ๐ญ๐ซ๐ข๐ง๐ .

my_string = "Hello, world!"
starts_with_hello = my_string.startswith("Hello")  # True
ends_with_world = my_string.endswith("world")  # False

๐Ÿ๐ŸŽ- ๐œ๐จ๐ฎ๐ง๐ญ(): ๐‚๐จ๐ฎ๐ง๐ญ๐ฌ ๐ญ๐ก๐ž ๐จ๐œ๐œ๐ฎ๐ซ๐ซ๐ž๐ง๐œ๐ž๐ฌ ๐จ๐Ÿ ๐š ๐ฌ๐ฎ๐›๐ฌ๐ญ๐ซ๐ข๐ง๐  ๐ข๐ง ๐š ๐ฌ๐ญ๐ซ๐ข๐ง๐ .

my_string = "apple, banana, orange, banana"
count = my_string.count("banana")  # count will be 2

Python pandas Complete
๐Ÿ‘‡๐Ÿ‘‡
https://topmate.io/codingdidi

Hope you'll like it

Like this post if you need more resources like this ๐Ÿ‘โค๏ธ
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Expand your job search to increase your chances of becoming a data analyst.

Here are alternative roles to explore:

1. ๐—•๐˜‚๐˜€๐—ถ๐—ป๐—ฒ๐˜€๐˜€ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Focuses on using data to improve business processes and decision-making.

2. ๐—ข๐—ฝ๐—ฒ๐—ฟ๐—ฎ๐˜๐—ถ๐—ผ๐—ป๐˜€ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Specializes in analyzing operational data to optimize efficiency and performance.

3. ๐— ๐—ฎ๐—ฟ๐—ธ๐—ฒ๐˜๐—ถ๐—ป๐—ด ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Uses data to drive marketing strategies and measure campaign effectiveness.

4. ๐—™๐—ถ๐—ป๐—ฎ๐—ป๐—ฐ๐—ถ๐—ฎ๐—น ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Analyzes financial data to support investment decisions and financial planning.

5. ๐—ฃ๐—ฟ๐—ผ๐—ฑ๐˜‚๐—ฐ๐˜ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Evaluates product performance and user data to help product development.

6. ๐—ฅ๐—ฒ๐˜€๐—ฒ๐—ฎ๐—ฟ๐—ฐ๐—ต ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Conducts data-driven research to support strategic decisions and policy development.

7. ๐—•๐—œ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Transforms data into actionable business insights through reporting and visualization.

8. ๐—ค๐˜‚๐—ฎ๐—ป๐˜๐—ถ๐˜๐—ฎ๐˜๐—ถ๐˜ƒ๐—ฒ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Utilizes statistical and mathematical models to analyze large datasets, often in finance.

9. ๐—–๐˜‚๐˜€๐˜๐—ผ๐—บ๐—ฒ๐—ฟ ๐—œ๐—ป๐˜€๐—ถ๐—ด๐—ต๐˜๐˜€ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Analyzes customer data to improve customer experience and drive retention.

10. ๐——๐—ฎ๐˜๐—ฎ ๐—–๐—ผ๐—ป๐˜€๐˜‚๐—น๐˜๐—ฎ๐—ป๐˜: Provides expert advice on data strategies, data management, and analytics to organizations.

11. ๐—ฆ๐˜‚๐—ฝ๐—ฝ๐—น๐˜† ๐—–๐—ต๐—ฎ๐—ถ๐—ป ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Analyzes supply chain data to optimize logistics, reduce costs, and improve efficiency.

12. ๐—›๐—ฅ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜€๐˜: Uses data to improve human resources processes, from recruitment to employee retention and performance management.


Hope this helps you ๐Ÿ˜Š
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Free website to learn and get certificates!!

https://panx.io/awesome-certificates/

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๐Ÿฅณ ๐Ÿฅณ Good news ๐Ÿ—ž๏ธ๐Ÿ—ž๏ธ ๐Ÿฅณ
I want to inform you that the MySQL classes ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿซ will be starting from 10th July ๐Ÿ—“๏ธ if you're interested please enroll in the classes ASAP ๐Ÿ”” as there are limited seats ๐Ÿ’บ.
Thank you โ˜บ๏ธ
Regards,
Codingdidi ๐Ÿ‘ฉ๐Ÿปโ€๐Ÿ’ป
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๐Ÿ‘‰๐Ÿ‘‰Template to ask for referrals(For freshers)

โค๏ธLike for more โค๏ธ


Hi [Name],

I hope this message finds you well.

My name is [Your Name], and I recently graduated with a degree in [Your Degree] from [Your University]. I am passionate about data analytics and have developed a strong foundation through my coursework and practical projects.
I am currently seeking opportunities to start my career as a Data Analyst and came across the exciting roles at [Company Name].

I am reaching out to you because I admire your professional journey and expertise in the field of data analytics. Your role at [Company Name] is particularly inspiring, and I am very interested in contributing to such an innovative and dynamic team.

I am confident that my skills and enthusiasm would make me a valuable addition to this role [Job ID / Link]. If possible, I would be incredibly grateful for your referral or any advice you could offer on how to best position myself for this opportunity.

Thank you very much for considering my request. I understand how busy you must be and truly appreciate any assistance you can provide.

Best regards,
[Your Full Name]
[Your Email Address]
โค8๐Ÿ‘6๐Ÿ”ฅ4
โœ…What roles make it easier to get into Data Science?

Most of Data Scientists usually transitioned in from other roles

The most common ones, are - Data Analyst, Business Intelligence Engineer and Data Engineer.

For a fresher with only a bachelors degree, I would advise the Data Analyst role. Based on the team and work, you may in essence be able to work as a Data Scientist.
โค6๐Ÿ‘1