Povilas Korop’s Post

How to learn data science -> build projects How to learn machine learning-> build projects How to learn web development -> build projects How to learn data analytics -> build projects Projects give you idea of how things actually work in real life. Also, give you added advantage of showcasing your learning to recruiters in future. Agree?

As data analysts, one of our biggest challenges is dealing with messy data. Incomplete datasets, inconsistent formatting, and data silos can slow us down. Personally, I find that building a strong data-cleaning process is key to tackling these issues. Tools like Python’s Pandas and PowerQuery have been lifesavers. CTEs have also helped in ‘doing the Lord’s work’ 😊 What is your biggest challenge with data, and how do you solve it? Let’s share some hacks!

How to learn data science -> build projects How to learn machine learning-> build projects How to learn web development -> build projects How to learn data analytics -> build projects Projects give you idea of how things actually work in real life. Also, give you added advantage of showcasing your learning to recruiters in future. Agree?

How to learn data science -> build projects How to learn machine learning-> build projects How to learn web development -> build projects How to learn data analytics -> build projects Projects give you idea of how things actually work in real life. Also, give you added advantage of showcasing your learning to recruiters in future. Agree?

Check out my blog post https://wix.to/1nffzUN hashtag#newblogpost 📊 Unlock the power of data analysis beyond just technical skills! Discover the essential aspects that will elevate your insights. Ready to level up your data game? Click https://wix.to/wJTH2Jh to learn more. #DataAnalysis #SkillDevelopment #DataInsights

🌟 Exciting Achievement Unlocked! 🌟 I’m thrilled to share that I’ve just earned the "Discover Data Analysis" badge from the Microsoft Data Analyst Career Path course! 🎓 This marks a key milestone in my journey to becoming a proficient Data Analyst, enhancing my skills in data exploration, visualization, and insights generation. I’m excited to apply these new skills to real-world projects and continue growing in the field of data analysis. A big thanks to everyone supporting me on this journey! 🚀 #DataAnalysis #Microsoft #CareerGrowth #ContinuousLearning #DataAnalyst #SQL #Python #Excel @MicrosoftLearn journey!

Have you ever looked at a massive data file and thought, “Where do I even start?” That was me—sorting through endless columns, trying to spot patterns and missing information. But then I found Pandas Profiling—and it felt like magic! With just one line of code, Pandas Profiling gives you a clear report on your data: 1. Quick Overview: It shows you the average, most common values, and any missing spots. 2. Easy Charts: Simple graphs make it easy to spot trends. 3. Find Issues Fast: It quickly points out any missing or unusual data. Here is the cheat sheet: pip install pandas-profiling import pandas as pd from pandas_profiling import ProfileReport df=pd.read_csv("city") Now, to create report: city_day=ProfileReport(df, title="city_day") city_day.to_file(output_file="city_day.html") If you work with data, Pandas Profiling makes it so much easier. Have you tried it? Let’s talk about it below! 😊 If you want to see how to do it, comment down below, i will provide the video to you in next post.

What do you mean by missing values? Missing data (or missing values) is defined as the data value that is not stored for a variable in the observation of interest. The problem of missing data is relatively common in almost all research and can have a significant effect on the conclusions that can be drawn from the data [1]. How do you select missing values in pandas? Using isnull() Method The simplest way to select rows with null values is to use the isnull() method. This method returns a boolean mask that indicates whether each element in the dataframe is null or not. We can then use this boolean mask to select the rows that have null values.

I dived into Pandas to refine my data manipulation skills. In this notebook, I explore practical use cases, from loading datasets to performing advanced operations, which include describing data with Pandas, selecting and viewing data, and data manipulation—cleaning up data, filling missing values, dropping empty columns, adding columns and shuffling. Key Takeaways: 1. Data Description: Learned how to summarize and understand the dataset quickly. 2. Data Selection: Got skilled at picking out the data needed with Pandas' selection tools. 3. Data Cleanup: Applied strong techniques to clean and organize data for better analysis. If you're curious about how Pandas can enhance your data manipulation, check out my notebook here - https://lnkd.in/dDbcAuVH

Do you want to become a Data Analytics expert but don't know where to start?   Start by applying the 1% rule.   The 1% rule states that you should aim to improve 1% every day.   So, if you're looking to become an Analytics expert, start with something small.   ⟶ Read a technical blog post or article. ⟶ Watch a tutorial video. ⟶ Solve a small programming problem.   Small daily improvements lead to massive results over time.   Remember, becoming an Analytics expert doesn't happen overnight.   It takes time, effort, and consistency.   So, start with 1% today and keep at it.   Who knows where you'll be in a year.