What is data science? In this digital era, data science can be best described as a beacon of knowledge, lighting up the path towards a future where data isn't just information, it's the key to unlocking unlimited possibilities.
As a beginner, in the field of data science, one has to:
1. Get comfortable with numbers
Duration: 1 month
A data scientist must have a good understanding of basic mathematical and statistical concepts e.g. algebra, probability and calculus.
2. Learn Coding Basics
Duration: 2 months
Coding in data science enables a scientist to create algorithms and build models. Python and R are the two most popular programming languages in the field of data science.
For a beginner, Python is the most recommended language because its syntax is easy to read and comprehend.
3. Play with Data
Duration: 2 months
Playing with real data gives you exposure to the challenges and excitement of data science. You harness your skills in handling messy data and discover meaningful patterns.
Pandas is a Python library that provides data structures and data analysis tools that are easy to use.
When playing with data using Pandas, you should learn how to:
Load data. Learn to import data from varied formats like SQL, CSV, and Excel into Pandas data frame and use functions like '.head()' and '.describe()' to understand the content of a dataset.
Clean data. Learn how to delete rows with missing values by using the '.dropna()' method, pinpoint and delete duplicate entries using the '.drop_duplicates()' method, and use the '.astype()' function to confirm that all columns are in the correct data types.
Explore data. Learn how to use boolean indexing to extract specific columns and rows based on the set conditions, use '.groupby()' function to categorize data based on specific features, and create basic visualizations.
4. See Data Visually
Duration: 1 month
Visualizations bring data to life since they create images that are easy to comprehend and trends and outliers can be identified at a glance.
Matplotlib is a library in Python that is used to create static, animated, and interactive visualizations. It is suitable for both beginners are professionals.
5. Start Machine Learning Journey
Duration: 3 months
Machine Learning is the art of training computers to learn patterns through algorithms and statistical models.
To start off your journey, you should; understand basic concepts like "learning from data" and try simple algorithms e.g. linear regression, classification, and decision trees.
Scikit-Learn (Python) offers an easy-to-use, accessible, and efficient tool for data mining and data analysis.
6. Do Projects
Duration: 2 months
Once you have grasped all the above-mentioned concepts. Identify a specific project and embark on it so that you can put your mastery to test.
7. Learn Basic Database Concepts
Duration: 2 months
A database is an organized collection of structured data or information. SQL(Standard Query Language) is a language used to create, manipulate, and analyze data.
A data scientist should understand SQL basics i.e. JOIN - Combine data from multiple tables, SELECT - Extract data from a database, among others.
8. Be curious and exploratory
Duration: Never Ending
Data Science is a field that is developing gradually. In order to catch up with its emerging trends, one has to be curious and keep on practicing in order to sharpen your skills.
Fields that one can explore include; Advanced Predictive Modelling, Real World Applications (Natural Language Processing), and Advanced Technologies(Deep Learning and Big data).
In your journey of learning data science, expect to make an uncountable number of mistakes. These mistakes are chances to grow, refine methods, and learn. Brace yourself, embrace challenges, celebrate successes, and learn from failures. The joy of discovery and problem-solving makes the data science journey exciting. Stay curious, persist, and relish every step!
Top comments (1)
Nice work Mel