I hate to tell you this but...

Bootcamps that tell you they can get you a 6-figure data analyst job within 6 weeks (or even 6 months) are lying to you.

Don't focus on the salary that you might get.

Instead, focus on...

- learning the tools
- starting your portfolio
- revamping your resume
- getting active on LinkedIn
- putting the skills into practice

I guarantee you'll be more successful.

Someone asked me today if they need to learn Python & Data Structures to become a data analyst. What's the right time to start applying for data analyst interview?

I think this is the common question which many of the other freshers might think of. So, I think it's better to answer it here for everyone's benefit.

The right time to start applying for data analyst positions depends on a few factors:

1. Skills and Experience: Ensure you have the necessary skills (e.g., SQL, Excel, Python/R, data visualization tools like Power BI or Tableau) and some relevant experience, whether through projects, internships, or previous jobs.

2. Preparation: Make sure your resume and LinkedIn profile are updated, and you have a portfolio showcasing your projects and skills. It's also important to prepare for common interview questions and case studies.

3. Job Market: Pay attention to the job market trends. Certain times of the year, like the beginning and middle of the fiscal year, might have more openings due to budget cycles.

4. Personal Readiness: Consider your current situation, including any existing commitments or obligations. You should be able to dedicate time to the job search process.

Generally, a good time to start applying is around 3-6 months before you aim to start a new job. This gives you ample time to go through the application process, which can include multiple interview rounds and potentially some waiting periods.

Also, if you know SQL & have a decent data portfolio, then you don't need to worry much on Python & Data Structures. It's good if you know these but they are not mandatory. You can still confidently apply for data analyst positions without being an expert in Python or data structures. Focus on highlighting your current skills along with hands-on projects in your resume.

Hope it helps :)

Websites to practice SQL queries
👇👇
https://www.linkedin.com/posts/sql-analysts_learning-sql-is-not-enough-you-need-to-practice-activity-7217873462596386816-E65q?

Like for more ❤️

Complete Roadmap to learn SQL in 2024 👇👇
https://t.iss.one/free4unow_backup/831

SQL Mindmap
https://www.linkedin.com/posts/sql-analysts_many-people-charge-too-much-to-teach-sql-activity-7219679815837057024-DAQc?

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.

SQL Window functions

Power BI Learning Plan in 2024
👇👇
https://t.iss.one/sqlspecialist/745

Guys, please avoid making excuses or procrastinating. The provided data analytics resources are more than sufficient to start your journey in this field. Stay focused, be consistent, and make the most of these materials. If you're unsure where to start, begin with the SQL tutorials. I'll also include resources for practicing SQL problems online.

The key is to take the initiative. Once you start, you'll better understand how everything works. Engage in the hands-on projects mentioned in the sessions. I'll try enhancing this product in the future without requiring any extra cost.

Feel free to reach out to me if you need any help or guidance. All the best for your future 👍👍

Data Analytics Dream Song
👇👇
https://youtu.be/WCTBf7zfq9M?si=76NhVgI0TaB4v_OJ

If you're thinking about building a data analytics projects, you don't need another book, video, or blog post.

Just start.

You'll learn 10x more by failing big time than by reading someone else's advice 🤷♂️

Starting exploratory data analysis (EDA) can be tricky. Many of us often feel lost at the beginning. Here's a simple way to get on track: start by creating hypothesis questions and defining KPIs based on your dataset and the field you are working in.

𝐅𝐨𝐥𝐥𝐨𝐰 𝐭𝐡𝐞𝐬𝐞 𝐬𝐭𝐞𝐩𝐬 𝐭𝐨 𝐠𝐮𝐢𝐝𝐞 𝐲𝐨𝐮𝐫 𝐄𝐃𝐀:
1. 𝑼𝒏𝒅𝒆𝒓𝒔𝒕𝒂𝒏𝒅 𝒀𝒐𝒖𝒓 𝑭𝒊𝒆𝒍𝒅: Learn about the industry and the specific problems you're trying to solve. This will help you know what to look for in your data.
2. 𝑰𝒅𝒆𝒏𝒕𝒊𝒇𝒚 𝑲𝒆𝒚 𝑴𝒆𝒕𝒓𝒊𝒄𝒔: Decide on the most important KPIs for your analysis. These should align with your business goals and provide clear insights.
3. 𝑪𝒓𝒆𝒂𝒕𝒆 𝑯𝒚𝒑𝒐𝒕𝒉𝒆𝒔𝒆𝒔: Formulate questions that your EDA will try to answer. This keeps your analysis focused and purposeful.

Using these steps will make your EDA process smoother and ensure your results are valuable and relevant.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 V/S 𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐀𝐧𝐚𝐥𝐲𝐬𝐭 (𝐁𝐀):

- Acts as a bridge between the business side and the IT side of an organization.
- Gathers and analyzes business requirements.
- Conducts stakeholder meetings.

𝐁𝐮𝐬𝐢𝐧𝐞𝐬𝐬 𝐈𝐧𝐭𝐞𝐥𝐥𝐢𝐠𝐞𝐧𝐜𝐞 (𝐁𝐈):

- Focuses on data analysis, reporting, and data visualization using BI tools.
- Extracts and transforms data from various sources into meaningful insights to support decision-making.
- Builds dashboards and reports.
- Identifies trends and patterns in data.

𝐄𝐱𝐚𝐦𝐩𝐥𝐞:

𝐀𝐦𝐚𝐳𝐨𝐧: A BA might analyze customer feedback to improve delivery processes, while a BI professional could create dashboards to monitor sales trends and warehouse efficiency.

𝐆𝐨𝐨𝐠𝐥𝐞: A BA could work on improving user experience based on app usage data, whereas a BI expert might analyze advertising data to optimize ad campaigns.

🥳🚀When delving into data analytics and initiating your SQL journey, prioritize mastering the fundamental concepts that address the majority of problems before delving into other topics.

👉🏻 Basic Aggregation function:
1️⃣ AVG
2️⃣ COUNT
3️⃣ SUM
4️⃣ MIN
5️⃣ MAX

👉🏻 JOINS
1️⃣ Left
2️⃣ Inner
3️⃣ Self (Important, Practice questions on self join)

👉🏻 Windows Function (Important)
1️⃣ Learn how partitioning works
2️⃣ Learn the different use cases where Ranking/Numbering Functions are used? ( ROW_NUMBER,RANK, DENSE_RANK, NTILE)
3️⃣ Use Cases of LEAD & LAG functions
4️⃣ Use cases of Aggregate window functions

👉🏻 GROUP BY
👉🏻 WHERE vs HAVING
👉🏻 CASE STATEMENT
👉🏻 UNION vs Union ALL
👉🏻 LOGICAL OPERATORS

Other Commonly used functions:
👉🏻 IFNULL
👉🏻 COALESCE
👉🏻 ROUND
👉🏻 Working with Date Functions
1️⃣ EXTRACTING YEAR/MONTH/WEEK/DAY
2️⃣ Calculating date differences

👉🏻CTE
👉🏻Views & Triggers (optional)

Here is an amazing resources to learn & practice SQL: https://t.iss.one/sqlanalyst/195

Hope it helps in your SQL learning 📚

Will AI Tools for Data Analysis Replace Data Analysts?

AI and Data Analysis are two closely related scientific areas, that have been developing rapidly for the last several years. As technology continues to evolve, the question arises: Will AI tools for data analysis replace data analysts?

This article aims to describe how AI is related to Data Analysis, what it can do, and will AI tools for data analysis replace data analysts. Starting with the introduction to AI and its fundamental aspects, to how it is going to affect the world in the distant future, the article addresses that and also focuses on how AI is associated with Data analysis.

The moderate generation of AI comprises Machine Learning, Deep Learning, and Generative AI. While generative AI is the capability to produce materials and contents like images, sound, and music, Machine Learning is a specific type of GI that prepares an algorithm to feed information to make a prediction.