How to get started with data science

Many people who get interested in learning data science don't really know what it's all about.

They start coding just for the sake of it and on first challenge or problem they can't solve, they quit.

Just like other disciplines in tech, data science is challenging and requires a level of critical thinking and problem solving attitude.

7 things you should know before becoming a Data Scientist:

7/ Higher complexity solutions =/= higher impact solutions.

6/ The best Data Scientists do much more than Data Science. They lead product teams, they talk to customers, they build pipelines etc.

5/ You won’t get along with every business partner. But you have to learn how to work with them.

4/ A lot of Data Science work is tedious and boring and repetitive.

3/ You will spend so much more time on communication than you expect.

2/ Data quality is often more important than fancy algorithms.

1/ You’ll make mistakes, a lot of it. What matters more is how you recover and grow from them.

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.

Fastest way to excel at Data Interviews:

Take as many Interviews as possible.

Don't be too picky with the roles you apply for as a beginner. Cast a wide net and apply for every data-related position you can find.

What's the worst that could happen?

You might get rejected. So what?

Remember:

☑️ Each interview is a learning opportunity

☑️ You'll refine your coding skills with every technical round

☑️ Your data visualization explanations will get clearer each time

☑️ You'll get more comfortable discussing your projects and impact.

There are 2 types of data enthusiasts out there:

Those who ace data analyst interviews and those who don't apply enough.

💡 Pro Tip: Keep an "interview journal" to note what worked, what didn't, and areas for improvement. Your future self will thank you!

10 great Python packages for Data Science not known to many:

1️⃣ CleanLab

Cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset.

2️⃣ LazyPredict

A Python library that enables you to train, test, and evaluate multiple ML models at once using just a few lines of code.

3️⃣ Lux

A Python library for quickly visualizing and analyzing data, providing an easy and efficient way to explore data.

4️⃣ PyForest

A time-saving tool that helps in importing all the necessary data science libraries and functions with a single line of code.

5️⃣ PivotTableJS

PivotTableJS lets you interactively analyse your data in Jupyter Notebooks without any code 🔥

6️⃣ Drawdata

Drawdata is a python library that allows you to draw a 2-D dataset of any shape in a Jupyter Notebook.

7️⃣ black

The Uncompromising Code Formatter

8️⃣ PyCaret

An open-source, low-code machine learning library in Python that automates the machine learning workflow.

9️⃣ PyTorch-Lightning by LightningAI

Streamlines your model training, automates boilerplate code, and lets you focus on what matters: research & innovation.

🔟 Streamlit

A framework for creating web applications for data science and machine learning projects, allowing for easy and interactive data viz & model deployment.

Data Science Job Expectation VS Reality!

Today, let's talk about real experiences working in data science. Sometimes, what we expect from a data science job may not match the reality of the day-to-day work. Let's explore this contrast between expectation and reality.

🎯Expectation: "I'll spend most of my time building fancy machine learning models and solving difficult problems."

📊Reality: While building and improving models is important, a big part of a data scientist's job is preparing and cleaning data. This involves organizing data, dealing with missing information, and making sure it's accurate. It requires attention to detail and careful work.

🎯 Expectation: "I'll work on groundbreaking projects that have a big impact."

📊 Reality: Data science projects often involve making small improvements and working step by step. You'll spend time analyzing data, finding patterns, and using data to make informed recommendations. Remember, many small wins can lead to significant positive outcomes.

🎯 Expectation: "I'll use the latest and coolest tools and technologies."

📊 Reality: While data scientists get to work with different tools and technologies, not every project needs the newest and trendiest ones. Depending on the project requirements, you may use well-established tools and focus more on solving problems rather than always exploring new technologies.

🎯 Expectation: "I'll work mostly with data."

📊Reality: Data science is a collaborative field. You'll work with people from different backgrounds, like experts in specific fields, engineers, and decision-makers. You'll need to understand business needs, share findings, and explain complex ideas to non-technical people. Communication and teamwork skills are important.

🎯Expectation: "I'll always be learning and keeping up with the latest research."

📊Reality: Learning is important, but it's also essential to balance staying updated with using existing knowledge effectively. The field changes quickly, so focusing on core concepts, gaining practical experience, and applying existing techniques to new problems are valuable skills.