If you are a data analyst and thinking of getting started with freelancing on upwork then here's something you should know.

You should be ready to invest money if you want to get started with freelancing on upwork.

So there's something called connects on Upwork. For simplicity you can consider connects as the currency of upwork which one will spend while submitting a proposal for the freelancing tasks listed on the platform.

Previously upwork used to give some free connects to every new account but these days they don't. So you have to buy the connects at the rate of 100 connects per $15 + Taxes (without upgrading to upwork plus) which will be 1.3k + taxes in INR.

Let's say you submit proposal for those jobs asking for 20 connects, the max you will be able to submit is 5 jobs and you will get the job or not again depend on many factors.
You may end up having no jobs even after spending 100 connects and then again you have to repeat the cycle.

Everything looks shiny from outside but reality can be different.
Every platform requires investment either in the form of time, dedication, money or combination of all.

SQL Zero to Hero
https://www.linkedin.com/posts/sql-analysts_sql-dataanalytics-activity-7210677005111984128-H5Oa?

If you want to earn 6-figures working as a data analyst, learn these 6 important skills:

Excel - advanced Excel functions for data manipulation and interpretation.

Data Cleaning is about mastering data preprocessing and cleaning techniques.

Python/R - data analysis, preparation and manipulation

Statistical Analysis - understanding fundamental statistics for data

Data Visualization - clear and effective visual representations of data

SQL - querying and managing databases efficiently

I'm sure you had an idea, but something got in the way and you didn't develop it. The channel "Usual thing" is about this, the author tries to implement different business ideas, but every day he encounters problems and discusses them with you.
https://news.1rj.ru/str/usual_thing

1. What are the various types of refresh options provided in Power BI?

Package refresh - This synchronizes your Power BI Desktop or Excel file between the Power BI service and OneDrive, or SharePoint Online.
Model or data refresh - This refreshes the dataset within the Power BI service with data from the original data source.
Tile refresh - This updates the cache for tile visuals every 15 minutes on the dashboard once data changes.
Visual container refresh - This refreshes the visible container and updates the cached report visuals within a report once the data changes.

2. Explain some date manipulation functions in SQL.

Getdate: As its name suggests, the getdate function gives us today’s date. Dateadd: The dateadd function is used for adding a time or date interval to a date.Datediff: The datediff function is used for calculating the difference between two dates based on a given interval. Datename: The datename function can be used for extracting the parts of a date. Year, month, day: The year, month, and day functions allow for decomposing a date.


3. What is CTE in SQL?

A CTE (Common Table Expression) is a one-time result set that only exists for the duration of the query. It allows us to refer to data within a single SELECT, INSERT, UPDATE, DELETE, CREATE VIEW, or MERGE statement's execution scope. It is temporary because its result cannot be stored anywhere and will be lost as soon as a query's execution is completed.

TOP CONCEPTS FOR INTERVIEW PREPARATION!!

🚀TOP 10 SQL Concepts for Job Interview

1. Aggregate Functions (SUM/AVG)
2. Group By and Order By
3. JOINs (Inner/Left/Right)
4. Union and Union All
5. Date and Time processing
6. String processing
7. Window Functions (Partition by)
8. Subquery
9. View and Index
10. Common Table Expression (CTE)


🚀TOP 10 Statistics Concepts for Job Interview

1. Sampling
2. Experiments (A/B tests)
3. Denoscriptive Statistics
4. p-value
5. Probability Distributions
6. t-test
7. ANOVA
8. Correlation
9. Linear Regression
10. Logistics Regression


🚀TOP 10 Python Concepts for Job Interview

1. Reading data from file/table
2. Writing data to file/table
3. Data Types
4. Function
5. Data Preprocessing (numpy/pandas)
6. Data Visualisation (Matplotlib/seaborn/bokeh)
7. Machine Learning (sklearn)
8. Deep Learning (Tensorflow/Keras/PyTorch)
9. Distributed Processing (PySpark)
10. Functional and Object Oriented Programming

Like ❤️ the post if it was helpful to you!!!

Free Resources for Numpy and Pandas:

Codebasics Numpy playlist: 
https://www.youtube.com/playlist?list=PLeo1K3hjS3uset9zIVzJWqplaWBiacTEU

Codebasics pandas playlist (first 9): 
https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy

Freecodecamp matplotlib playlist: 
https://youtu.be/3Xc3CA655Y4

Seaborn tutorials: 
https://youtu.be/GcXcSZ0gQps

Pandas for beginners
https://news.1rj.ru/str/datasciencefun/660

Numpy for beginners
https://news.1rj.ru/str/datasciencefree/156

Data Analyst Remote Job will be posted in this channel today
👇👇
https://news.1rj.ru/str/jobs_SQL

1. Define the term 'Data Wrangling.

Data Wrangling is the process wherein raw data is cleaned, structured, and enriched into a desired usable format for better decision making. It involves discovering, structuring, cleaning, enriching, validating, and analyzing data. This process can turn and map out large amounts of data extracted from various sources into a more useful format.

2. What are the best methods for data cleaning?

Create a data cleaning plan by understanding where the common errors take place and keep all the communications open. Before working with the data, identify and remove the duplicates. This will lead to an easy and effective data analysis process.Focus on the accuracy of the data. Set cross-field validation, maintain the value types of data, and provide mandatory constraints.Normalize the data at the entry point so that it is less chaotic. You will be able to ensure that all information is standardized, leading to fewer errors on entry.


3. Explain the Type I and Type II errors in Statistics?

In Hypothesis testing, a Type I error occurs when the null hypothesis is rejected even if it is true. It is also known as a false positive.

A Type II error occurs when the null hypothesis is not rejected, even if it is false. It is also known as a false negative.

4. How do you make a dropdown list in MS Excel?

First, click on the Data tab that is present in the ribbon.Under the Data Tools group, select Data Validation.Then navigate to Settings > Allow > List.Select the source you want to provide as a list array.

5. State some ways to improve the performance of Tableau?

Use an Extract to make workbooks run faster.
Reduce the scope of data to decrease the volume of data.
Reduce the number of marks on the view to avoid information overload.
Hide unused fields.
Use Context filters.
Use indexing in tables and use the same fields for filtering.
Remove unnecessary calculations and sheets.

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 cancel 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.

Data Analysis with Excel
👇👇
https://news.1rj.ru/str/excel_analyst/2

Power BI DAX Functions
👇👇
https://news.1rj.ru/str/PowerBI_analyst/2

All about SQL
👇👇
https://news.1rj.ru/str/sqlanalyst/29

Python for data analysis
👇👇
https://news.1rj.ru/str/pythonanalyst/26

Statistics Book and other useful resources
👇👇
https://news.1rj.ru/str/DataAnalystInterview/34

Here are top 40 commonly asked pyspark questions that you can prepare for interviews
👇👇
https://news.1rj.ru/str/sql_engineer/79

These are the top 5 skills (I think) you need as an entry-level data analyst:

1. Excel. It may not be fancy but it's still one of the most used tools in the business world. I can guarantee you will use it at some point.

2. SQL. You may not actually use SQL but it's worth learning. It's the language of databases and gives you a strong foundation for working with other data analysis tools.

3. A data viz tool. Look, I don't care if you learn Power BI, Tableau, or any other data viz tool. You need to be able to communicate insights in a way that makes sense to non-technical people.

4. Communication. This may actually be the most important skill. It doesn't matter if you can analyze data if you can't communicate why that analysis should matter.

5. Problem solving. You use data to answer business questions and...wait for it... solve problems. It's an absolutely essential skill to have.

The best part of this is that you very likely already have 2, if not 3, of these in a pretty good place.

Focus your efforts on the skills that will make a difference.

Data Analyst Interview Questions

Struggling to stay motivated in your job search?

Try setting input goals first, then shift to output goals once you’re consistent.

Let me explain how this works with a real-life example.

Input Goals vs. Output Goals:

When starting, focus on input goals to build consistency.

For instance, if you're struggling to go to the gym, set a goal to show up every other day rather than aiming to lose 50 pounds.

Once you’re consistent, shift to output goals like losing 5 pounds a month.

Why This Works:

- Focus and Pressure: Output goals create a sense of urgency and focus.
- Efficiency: You find faster and more effective ways to achieve your goals.
- Persistence: Sticking with a strategy until it works builds resilience and problem-solving skills.

Action Time:

1) Start with Input Goals: If you're struggling with consistency, set small, manageable goals to build habits.

2) Shift to Output Goals: Once you’re consistent, set specific, measurable outcomes.

3) Don't Quit: Commit to your goals and find ways to make them work.

Many people ask this common question “Can I get a job with just SQL and Excel?” or “Can I get a job with just Power BI and Python?”.

The answer to all of those questions is yes.

There are jobs that use only SQL, Tableau, Power BI, Excel, Python, or R or some combination of those.

However, the combination of tools you learn impacts the total number of jobs you are qualified for.

For example, let’s say with just SQL and Excel you are qualified for 10 jobs, but if you add Tableau to that, you are qualified for 50 jobs.

If you have a success rate of landing a job you’re qualified for of 4%, having 5 times as many jobs to go for greatly improves your odds of landing a job.

Does this mean you should go out there and learn every single skill any data analyst job requires?

NO!

It’s about finding the core tools that many jobs want.

And, in my opinion, those tools are SQL, Excel, and a visualization tool.

With these three tools, you are qualified for the majority of entry level data jobs and many higher level jobs.

So, you can land a job with whatever tools you’re comfortable with.

But if you have the three tools above in your toolbelt, you will have many more jobs to apply for and greatly improve your chances of snagging one.

𝐋𝐢𝐬𝐭 𝐨𝐟 𝐜𝐨𝐦𝐩𝐚𝐧𝐢𝐞𝐬 𝐭𝐡𝐚𝐭 𝐡𝐢𝐫𝐞 𝐝𝐚𝐭𝐚 𝐚𝐧𝐚𝐥𝐲𝐬𝐭𝐬:
TMcKinsey & Company
Boston Consulting Group (BCG)
Bain & Company
Deloitte
PwC
Ernst & Young (EY)
KPMG
Accenture
Google
Amazon
Microsoft
IBM
Oracle
Tiger Analytics
Mu Sigma
Fractal Analytics
EXL Service
ZS Associates
Wells Fargo
Walmart
Target
LTIMindtree
Infosys
TCS (Tata Consultancy Services)
Wipro
HCL Technologies
Capgemini
Cognizant

These companies often hire data analysts to use data for making decisions and planning strategically for their clients.