Learn data science step by step at home for free
Whether you are new to programming or not, the information on the internet can baffle you a bit. There are free-of-cost resources available for you to learn data science at home.
Data science requires no field, it requires a keen interest. So, whether you are a beginner or you come from a different field, you can still understand data science step by step.
Step 1 Excel
Basic concepts you will be working with first are –
- Pivot table
- VBA macros
- data filters
- Charts and plots
Excel is an important tool to conquer data science. You can start using excel by managing your personal projects related to your finances.
Click on the link on Google docs for finances and it will show you templates where you can track your monthly income and expenses. It will project how much you are spending and how much you need to save. To create a visualization of your balance or project bar charts, learn the formulas. As a data analyst, you are going to work on this kind of visualization.
This way you can create your own budget tracker.
Step 2 Statistics
Statistics and probability include terms like standard deviation, mode, range, descriptive statistics, inferential statistics, normal distribution conditions before machine learning, and data analysis. If you already have a solid understanding spend a week or two brushing up on key concepts. Check out the Pandas Docs, Numpy Docs, and Matplotlib Tutorials. These are some of the resources I suggest. If you want to get used to these libraries, you will have to start using them on daily basis.
Start with these concepts and hop on to the advanced projects later on.
Step 3 Programming Language
Programming language is basically instructing your computer. Thousands of programming languages have been created and many more are stepping into the market with time. Usually, R or Python is the programming languages suggested for fresher. If you have no technical knowledge at all, it is better for you to go for Python as it is more versatile and will give you a good start in this career. Programming language is not as hard as it looks, just tries to learn it like any other language.
For learning the basics of programming language, you can audit courses or get certified if you want. You can find various programming courses here.
Step 4 Tools for Data Exploration
Tools like NumPy, pandas, data visualization help with data cleaning and exploration. Data cleaning is actually very time-consuming for a data analyst. Clean data is necessary for perceptive data analysis. Data cleansing, data cleaning, or data scrubbing is the first step in the overall data preparation process. Data that is messy or raw is analyzed and rectified in this process.
Data cleaning helps in filling in missing values, fixing mistakes & errors, and proofreading if all the information is in the right place or if the rows and columns need to be organized. Cleaning data is crucial to data analysis. Data cleaning is the foundation of efficient, accurate, and effective data analysis. Without cleaning data beforehand, the analysis process will not be precise because the information in the dataset will be untidy and cluttered.
Step 5 Machine Learning
For a data analyst, machine learning may not be as crucial as one thinks. If you are going for a data scientist’s role then machine learning will be required.
Machine learning compromises concepts of maths and statistics that may be difficult for some people. So it’s better not to spend months on step 2 rather, learn the statistics that are required along with machine learning in a week or two. For mathematics, 3b1b is a very interactive youtube channel that will teach you maths through visualization.
You can also get certified in machine learning from Edureka.
Step 6 Deep Learning
It starts with an artificial neural network and goes further to the convolutional neural network which is majorly used for image processing, then we move into RNN i.e. recurrent neural network used for NLP
Deep learning is nothing but machine learning that is a human gaining certain knowledge. Deep learning is an important element of data science, it includes statistics and predictive modeling. If someone is targeting the career of data scientist, deep learning will be a huge help with collecting, analyzing, and interpreting large amounts of data.
The Machine learning course from Edureka will cover this also.
Step 7 – Business Intelligence (bi) tools
Some popular bi tools are Tableau, power bi, qlik sense, and these tools help you to connect with your data source. These tools are powerful for a career as a data analyst, you must have knowledge about these Some irrelevant data from internal and external systems such as books, journals, documents, health records, images, files, etc. are collected by these types of applications software.
You can create impressive reports, dashboards, and data visualization as these tools help create data for analysis.
The results can help data analyst to improve their decision-making skills, operational efficiency, identity marker trends, and new business opportunities..
All the steps are interconnected and you will have to revisit all the concepts. Data science is not something that you can learn just through reading or watching videos. Exercising every concept you’ve learned is important and when you become good at grasping everything related to data basis, you can start pairing projects as well as technically difficult projects.
Start learning with a single step today, exercise it and data science will be at your toes within six months.