There are 4 steps by which you can easily become a data scientist. This is how I did it.
Learn Statistics First
Statistics is the most important thing you need to learn to become data scientist. This is the primary requirement and it is not that hard as it seems, just give it a try.
We have 2 categories for statistics:-
- Descriptive statistics
- Types of data variables
- Central tendency measures
- Spread of data, skew of data
- Measures of dispersion
- Inferential statistics
- Population and sample (Sampling methods is optional but read it : simple random sampling and stratified random sampling
- Random variables, Probability distributions – normal, Poisson
- Estimation and Hypothesis testing.
Now you should learn some important statistics tool which really help in visualizing data better.
Learn Excel & Power BI: 750 million users globally, these tools are really important and makes our task easier.
- Relevance of inserting tables in excel – 9 great reasons to insert tables in excel for data analytics professionals.
- Consolidating data with compatibility view features
- Data manipulation with machine learning in excel using xlstat
- Analysis toolpak for descriptive and inferential statistics
- Power View, Power Query, Power Pivot and Power Maps.
Now you are ready for the next step that is learning the programming language either R or Python
Learn R and Python (Yes its and not or)
Yes you need to learn both of them and the only liberty you have to choose which one to learn first.
There is another important tool that needs to be understood before you call yourself a data scientist and it is “Tableau”.
Learn Tableau (it is integrated with Python and R)
Why? It’s too hot to ignore. Along with data exploration it’s important to visualize the data and it could be done in a best way using Tableau.
Welcome to Data Science, it’s a lovely world. This is all you need to do and you are ready to go. I came here three years ago and never regretted it. Start learning and Happy Learning.