3 Simple Steps How I Got My Power BI Certification

Data Science Nugget 🧽

Most people tend to make things more complex than they are.

They do everything except the one thing that will move them forward.

But I don’t blame them because most people in Data Science don’t have proper guidance.

They don’t have someone on their side who tells them exactly what they need to do or breaks down complex things into simple terms.

This is why you need proper guidance.

So where can you find that guidance?

Right here in this newsletter.

I will explain to you how to get a Power BI Certification as simply as possible.

So here are 3 steps to do that:

1. Self-Learn on the self-study track in Microsoft

2. Practice your learnings in Projects

3. Study the exam dumps to get ready

These are the same 3 steps that I myself followed to get my own certificate…

If I can do it, you can too.

You need to put in the reps and you’ll crush it!

Here’s some exam dumps to help you out

Interesting Dataset for Practice 📊

Sentiment Analysis Dataset

This comprehensive dataset is a meticulously curated collection of mental health statuses tagged from various statements

Project Ideas:

1) Basic Sentiment Analysis Model

• Build a simple machine learning model that classifies statements into one of the seven mental health statuses (e.g., Normal, Depression, Anxiety, etc.).

• Use Python with libraries like Scikit-learn or TensorFlow.

2) Word Cloud Generation

• Generate word clouds for each mental health status to visually analyze the most common words associated with each condition.

• Python with WordCloud and Matplotlib.

3) Text Preprocessing Pipeline

• Create a pipeline that preprocesses the text data by cleaning it (e.g., removing stopwords, and lemmatization) and prepares it for modeling.

• Python with NLTK or SpaCy.

Data Analysis Tool of the Week 🛠️

This week let’s look at a very useful Python library - NLTK

Natural Language Toolkit (NLTK) is the most popular library for natural language processing (NLP) which was written in Python and has a big community behind it.

NLTK is also very easy to learn, actually, it’s the easiest natural language processing (NLP) library that you’ll use.

Let’s look at some Basic functions of NLTK

Tokenization is the process of breaking text into words or sentences.

Stopwords are common words like "the," "and," and "in" that are often removed during NLP preprocessing.

Q&A Section 🙋

A member of the Data Science Master Mind Group recently asked me:

"Why is SQL query optimization essential? How to optimize SQL query? "

It’s important to fine-tune your queries as it makes your database operate more efficiently, saving time, resources, and money.

Here are some techniques:

Use Indexing: Indexes help speed up data retrieval by creating a quick reference to the rows in a table.

Optimize JOIN Operations: Ensure that you only JOIN the tables you need and use appropriate JOIN types (INNER, LEFT, RIGHT) based on your requirements.

Avoid Subqueries: Subqueries can impact performance especially if they are a complex operation.

Analyze and Optimize: Monitor the performance of your queries using tools like database profilers and query execution plans. Identify slow-performing queries and analyze their structure for potential improvements.

Once you start implementing the basics the more advanced stuff will start following.