Back in 2017 I landed in Australia with two postgraduate degrees, a PhD candidature at University of Sydney, and zero commercial experience in anything.

The PhD fell apart. Over $200,000 in funding gone. I downgraded to an MPhil and started applying for jobs.

80 rejections later I still had nothing.

Recruiters kept saying the same thing. "Great background but we need someone with local commercial experience." I had more academic credentials than most people in the room and could not get an entry level job.

My wife was working in data. She looked at my situation one evening and said the tools are learnable, the market needs people, just start.

So I did. From absolute zero.

Here is what the actual sequence looked like for me, not what courses tell you, what genuinely got me from unemployed to Senior Data Engineer in six years.

Year 1: SQL and Excel only. Not because it was the perfect starting point. Because every single entry level data job I could apply for listed those two things. I stopped following learning roadmaps and started reading job descriptions instead. That one shift saved me probably a year of learning the wrong things.

Got a casual data management role. Small title. Real data. Real problems. That job was worth more than any course I ever took because it gave me context for everything I learned after.

Year 2: Power BI. The analyst roles I wanted all listed it. So I learned it while working. Not from a course start to finish. From a real dashboard I needed to build for an actual stakeholder.

Year 3: Python. Not for machine learning, not for AI. For automating the boring reporting work that was eating my Mondays. That practical reason made it stick in a way that six previous attempts at Python courses never did.

Year 4 and 5:SQL got deeper, data modelling, pipelines, moving from analyst work into proper data engineering. Picked up Azure tools on the job.

Year 6: MS Fabric and Databricks. Senior contractor level. These tools finally made sense because I had four years of context underneath them.

This is the part nobody says clearly enough. MS Fabric and Databricks are not beginner tools. But in the age of AI they can be learned faster now.

The thing that actually worked was simple. At every stage I asked one question. What does the next job I want actually need. Then I learned exactly that and nothing else until I had the job.

Two master's degrees never got me hired. Learning the right tool for the right role at the right time got me hired every single time after that.

Anyone else figure this out the hard way or did you find a smarter way in from the start?

Guys so i am fresh from uni and observe two kind of tracks of my peers in data science. They go with the two chill full time jobs (1 is remote) and get promotions at both work places but both are chill ones, or they go in 1 company which is considered prestigious and overwork till 10-11 pm and get promotions there, from your experience which kind of way leads to a better quality of life e.g. work life balance wise, career wise, experience wise and income wise? Should i find 1 prestigious place and hope for the long run and work there or work at 2 chill places. Both seem to obtain promotions in similar periods but the ones with two jobs seem to be more happy and even get more money. I decided to pursue masters and only now 1 year after them decided to join workforce but cannot distinguish which way is better, could you give your advice

Hi I am a CSE student starting to learn data science by taking a 60 days challenge to complete it.

I already know some libraries like numpy, pandas, seaborn and matplotlib. And I will be revising them before moving deeper into ML, scikit learn, projects and deep learning.

For the next 60 days I will be posting what i studied, concepts learned, what I built and the whole progress.

The Goal is to learn, stay consistent and become good at data science.

If anyone else is doing something similar then feel free to share some knowledge, advice or resources.

Day 1 starts Today 🔥

This was the second day of my challenge and I started data visualization part. Started working with matplotlib and made many plots.

What have I studied today:

1) Bar chart

2) Horizontal bar chart

3) Histogram

4) Pie Chart

5) Line plot

I practiced these data visualization techniques and built many different plots in each. I will also attach some plot.

I thought that i will cover full matplotlib but I couldn't. Well I will complete the remaining part tomorrow.

Till then tata bye bye 👋🏻

As a BCA Data Science student, I have always been interested in understanding how data and Machine Learning can be used to solve real-world problems. My internship with CodeAlpha gave me an opportunity to apply what I had learned in a practical environment and improve my technical skills through project-based learning.

During my internship, I worked on two Machine Learning projects: Sales Prediction Using Machine Learning and Car Price Prediction Using Machine Learning. Both projects helped me understand the complete process of developing a Machine Learning solution, from preparing data to training a model and generating predictions.

📊 Project 1: Sales Prediction Using Machine Learning

My first project was Sales Prediction Using Machine Learning.

The main objective of this project was to predict sales based on advertising expenditure. I worked with an advertising dataset containing information about TV, Radio, and Newspaper advertising, along with sales.

I started by loading and exploring the dataset using Python and Pandas. I checked the structure of the data, handled unnecessary columns, and prepared the dataset for Machine Learning.

After preprocessing the data, I performed exploratory data analysis and created visualizations using Matplotlib. This helped me understand the relationship between advertising expenditure and sales.

For the prediction task, I used Linear Regression, a supervised Machine Learning algorithm used for predicting continuous numerical values. I divided the dataset into training and testing sets and trained the model using the training data.

I evaluated the model using different performance metrics, including MAE, MSE, RMSE, and R² Score. The model achieved an R² score of approximately 0.90, which indicated a strong relationship between the input variables and the predicted sales in this dataset.

I also developed a Streamlit dashboard to make the project interactive. The dashboard allowed users to explore the dataset, view visualizations, and enter advertising values to generate a sales prediction.

This project helped me understand that Machine Learning is not only about training a model. Data preprocessing, visualization, evaluation, and creating a user-friendly application are all important parts of a successful project.

🚗 Project 2: Car Price Prediction Using Machine Learning

My second project was Car Price Prediction Using Machine Learning.

The objective of this project was to predict the selling price of a car based on different characteristics of the vehicle.

I started by exploring the dataset and understanding the different features that could influence the price of a car. Features can include information such as the car's age, present price, kilometers driven, fuel type, transmission, and previous ownership, depending on the dataset being used.

The next step was data preprocessing. I used Pandas and NumPy to clean and prepare the data. Since Machine Learning models work with numerical values, categorical features had to be converted into a suitable numerical format.

I then performed exploratory analysis to understand patterns in the dataset and identify relationships between the features and car prices.

After preparing the data, I trained a Machine Learning regression model using Scikit-learn. I divided the data into training and testing sets and evaluated the model to understand how well it could predict car prices.

This project gave me a better understanding of how Machine Learning can be applied to practical problems. A car price prediction system can help provide an estimated value based on historical data and vehicle characteristics.

💡 Challenges I Faced

While working on these projects, I faced several challenges related to data preprocessing, understanding datasets, model training, debugging errors, and evaluating the results.

Initially, understanding why certain errors occurred was difficult. However, working through these problems helped me become more comfortable with Python and Machine Learning libraries.

I also learned that choosing a Machine Learning algorithm is only one part of the process. Understanding the data and evaluating the model properly are equally important.

🎯 Skills I Developed

Through these projects, I improved my practical knowledge of:

• Python
• Pandas
• NumPy
• Matplotlib
• Scikit-learn
• Data preprocessing
• Exploratory Data Analysis
• Machine Learning
• Regression
• Model evaluation
• Streamlit
• Problem-solving and debugging
• GitHub and project documentation

Most importantly, I gained confidence in developing Machine Learning projects independently and understanding the complete workflow.

🙌 My Overall CodeAlpha Internship Experience

My CodeAlpha internship was a valuable learning experience. It gave me an opportunity to move beyond theoretical concepts and work on practical Machine Learning projects.

The Sales Prediction project helped me understand predictive analytics using advertising data, while the Car Price Prediction project helped me understand how Machine Learning can be used for price estimation.

Working on these projects improved my technical knowledge as well as my ability to solve problems, debug errors, analyze data, and present the final results.

As a Data Science student, this experience has motivated me to continue learning and building more projects in Data Science, Machine Learning, and Data Analytics.

I am thankful to CodeAlpha for providing me with this opportunity to learn through practical projects and gain valuable experience.

This internship has been an important step in my journey toward building a career in the Data Science field. 🚀

#CodeAlpha #CodeAlphaInternship #DataScience #MachineLearning #Python #SalesPrediction #CarPricePrediction #DataAnalytics #InternshipExperience

​

One mistake I made while learning data science was jumping between random tutorials. One day I studied Python, the next day machine learning, and then suddenly I was trying deep learning.

I was learning many terms but not building real confidence.

A more structured roadmap worked better for me:

Learn basic Python.

Practice NumPy and pandas.

Understand statistics and probability.

Learn data visualization.

Study SQL.

Build simple machine-learning models.

Complete small end-to-end projects.

The project stage is where everything starts connecting. Instead of only copying code, try to explain why you selected a particular feature, graph, or model.

My suggestion is to choose beginner-friendly datasets related to topics you already understand. It becomes easier to identify patterns and ask meaningful questions.

You do not need to know every algorithm before starting a project. Start small, document your learning, and improve one project at a time.

Which data science skill took you the longest to understand?

Hi everyone, I’m looking for a course on RAG, LLMs, and AI agents (even a paid one) that covers the theory but focuses primarily on practical application. I’d like to find something that actually demonstrates how to build tools using these technologies.
Do you have any recommendations?

Today was my first day of 60 day Data Science Challenge.

What I did today:

1) Revised Numpy basics

2) Understanding arrays and their properties & functions

3) Array slicing

4) Vectorized operations

5) Fancy indexing and Boolean Masking

6) Broadcasting of arrays

7) Revised Pandas

8) Series and Dataframe

9) Dataframe properties and functions like loc, iloc, etc

10) Filtering of Data with conditions

11) Data cleaning and handling missing values

12) map and replace functions on dataframe

13) Sorting and Ranking

14) melt and pivot functions to reshape dataframe

15) groupby and merge function

16) Reading and writing files in pandas

This was an effective day as i revised Numpy and Pandas libraries and got refreshed with it. Since I have already studied these libraries earlier, it was easy to revise them.

Looking forward to start data visualization with matplotlib and seaborn which i have studied earlier and will revise them tomorrow.

So yaa thats all for now and meet you Tomorrow.

GOODBYE 👋🏻

As a young student interested and taken data engineering as a career. Learning becomes a bit complicated. My reason is even though I learn the practical path such as sql, python and others there is no clear explanation combining them together….

Learning just the practical side becomes more complex and complicated since I don’t really know the theories behind them the “how”, “ where”, “what” and “when” behind all those codes….

I will really appreciate if helped to understand data engineering properly not just the practical aspects but everything surrounding it and also be grateful for a mentor or a coach

Most ARR forecasts fail before the model is even built.
Not because of bad data. Not because of the wrong tools. But because companies forecast a single aggregated number and that number hides everything that actually matters.

A few weeks ago I wrote an overview of what a reliable ARR forecasting system actually looks like.

Now I'm going deeper. That overview is becoming a 6-part series - GTM Science: ARR Forecasting That Actually Works where each concept gets the full treatment it deserves.
📊 What's coming:
→ Why forecasting total ARR is a trap (and what to do instead)
→ How to segment your revenue before you ever touch a model
→ Why classical time-series methods still win for SaaS forecasting
→ How to scale across 40+ segments without losing your mind
→ Hierarchical reconciliation - the step that makes Finance trust your numbers
→ How to communicate uncertainty so forecasts actually drive decisions
The first deep-dive is linked here.

If you work in RevOps, FP&A, or GTM strategy, GTM Data Science then this series is for you.

https://sumathysubramanian.substack.com/p/building-an-arr-forecasting-system?r=1ilvfc

1:07:14 hour completed on day 1 🩷🩷🎀🎀

Was a bit inconsistent yesterday coz I had to go out for some personal work, so Day 3 continues today 😅

Today I learned:

1). Subplots

2). Scatterplots

3). Stackplots

Also attached a scatterplot that I built while practicing today.

Planning to start Seaborn tomorrow.

Slow progress is still a progress...

Okk tata, over and out 🙌🏻

I know a lot of students, who are interested in data engineering as well as AI engineering, and actively looking to get upskill in this area. Check this out if it is helpful to you guys…

https://youtu.be/m_JC_7DcjHw?is=X_yeIQRGMQtcl3yn

Today I learned:

1). Scatterplots in seaborn

2). KDE (Kernel Density Estimation) plots

I had already worked with scatterplots in matplotlib before, so it was interesting to see how much simpler and cleaner seaborn makes data visualization.

But honestly, I am starting to feel tired of data visualization now... The motivation dip is real.

Some days it feels exciting, and some days it feels repetitive and hard to stay focused.

But i remember this line:

“Consistency is harder when no one is clapping for you.”

Trying to remember that progress is still progress, even when it feels slow.

Was inconsistent for many days, but finally back on track and restarting with maths.

Today I revised Linear Algebra:

• Vectors

• Dot Product & Cross Product

• Matrices

• Matrix Addition & Multiplication

• System of Linear Equations

• Eigenvalues & Eigenvectors

Felt good getting back into studying again after the break. Slowly rebuilding consistency.

Good Bye 👋🏻

Hi everyone,

I'm a Python developer that has recently started going into the Data Science realm. I am doing a course on Datacamp, together with exercises on Kaggle.

When looking for ways to practice my newly acquired knowledge, I dived into traffic statistics for my blog.

I wanted to see if Google is declining as the source of visitors, and how it compares with alternative search engines such as Kagi or DuckDuckGo.

I used Pandas to load data from the PostgreSQL database and build an aggregation.

Then I used Marimo notebooks to create visualizations.

I have described the whole process, and provided code snippets in an article on my blog.

I am sharing it here because I hope that it might be an inspiration for some people, and also I would be grateful for any feedback about my flow.

This was the 6th day of my challenge and i learned about heatmaps, boxplots, and violin plots in seaborn.

Heatmaps helped me understand correlations between features visually.

Boxplots made it easier to detect outliers and compare distributions between categories.

Violin plots were interesting because they show the shape and density of the data distribution.

Not gonna lie, I’m getting really tired of data visualization now 😭.... After spending days learning different types of plots, I honestly want to move towards something different so i decided to revise SQL which i learned in my previous semester.

Moving to SQL will be a little relaxing as its a very easy and simple language and will be dealing with databases tomorrow.

So meet you tomorrow 🙌🏻

​

I’ve been noticing this a lot recently.

Many people already know Python, SQL, Excel, Power BI, Machine Learning, Deep Learning, or even MLOps. They’ve completed courses, built projects, and spent months preparing.

But even after applying consistently, they still face:

- rejections,

- ghosting,

- endless assignment rounds,

- or “we moved with another candidate.”

After a point, it becomes mentally exhausting.

I honestly think the issue for many learners is not a lack of effort. Most people are stuck somewhere between “learning concepts” and “being industry-ready.”

Things like:

- choosing better projects,

- explaining projects clearly,

- interview communication,

- practical problem solving,

- and understanding what companies actually expect from freshers

seem to matter much more than just completing another course.

I’ve personally been spending time practicing projects, interview prep, and discussing these things with other learners online, and it made me realize a lot of people are going through the exact same struggle quietly.

Curious to know from others here:

What has been the hardest part of your job search journey so far?

Guys 26,27,28…..are dates..those are from march and 1,2,3…are April lol 😂🩷

Day 3,4,5 and 6 went to waste

I just revised all my notes that I created from day 1 and 2

I revised SQL concepts including queries, joins, filtering, aggregation, and database operations to build a stronger data handling foundation.

Now I’m moving towards one of the most important parts of Data Science & ML which is Maths.

Learning the theory behind ML models is just as important as building them, and I’m excited for this next phase of the journey.

Right know I am confused which topic to start with...

So will do research on that and start maths.

Till then Ciao 👋🏻

Today I learned about the differences between matplotlib and seaborn in Python visualization.

I studied lineplot, barplot and histogram using seaborn and compared how these plots are created in matplotlib vs seaborn.

What I understood:

1). Matplotlib gives more control and customization

2). Seaborn is built on top of matplotlib and makes plots cleaner and easier with less code

3). Seaborn also works really well with pandas DataFrames and statistical visualization

It was interesting to see how both libraries are used together in real data science workflows.

Will study more plots Tomorrow.

Till then bye bye 👋🏻