Alvin MustafaBuilding a successful career in data science involves acquiring the right education, and necessary skills and searching for job opportunities.
Below is a guide:
EDUCATION
Becoming a data scientist requires having skills in:
computer science: programming, statistics.
Mathematics: Probability and Statistics, and Linear algebra.
The above can be acquired by:
Pursuing a Barchelers degree in a relevant field such as Computer Science, Mathematics or Data Science.
Online Course and Certifications. Platforms like W 3 Schools, Freecode Camp, Datacamp and Coursera offer courses and certifications.
Bootcamps: Bootcamps such as Moringa School, LUX Academy and Data Science East Africa offer short-term programs that can help you acquire practical skills and experience.
Skills For a Data Scientist
Some of the key skills for Data Scientist are:
Programming
Programming languages such as Python and R are very essential for a data scientist to sort, analyze, visualize and manage large volumes of data(Big data).
Popular programming languages for data science include:
- Python
- R
- SQL
Probability and Statistics
Data scientists should fully comprehend mathematical concepts such mean, mode, median variance and standard deviation.
Some of the Statistical techniques you should know include:
- Normalization of data
- Dimensionality Reduction
- Over and under sampling
Data Wrangling
Data wrangling is the process of cleaning, transforming and preparing raw data into usable format for analysis.Manipulating the data to categorize it by patterns, trends and correct any input values can use a lot of time but is necessary to make data-driven decisions.
Key Steps in data wrangling are:
- Data Extraction: Gathering data from various sources such as Databases, CSV files, and web scraping.
- Data Cleaning: Detect errors in data and rectify them when possible.
- Data Transformation: Summarization of data and normalization.
Database Management
It is a crucial skill in data science as it involves effective handling of big data. This skill includes various aspects such as data storage, retrieval, and manipulation and it ensures data is accessible, organized and usable for analysis.
Database management tools include:
- MySQL
- MongoDB
- Oracle
Machine Learning and Deep Learning
This technique concentrates on creating and implementing algorithms that let machines learn from and make decisions based on data.
Practical Projects: Work on real-life data science projects. You can use platforms such as Kaggle to acquire real-life data.
Essential tools
Data analysis tools such as Pandas and Numpy.
Data visualization tools like Matplotlib Seaborn and Tableau.
Machine Learning Libraries like Scikit-learn.
Command Line like Git and Bash.
Job Searching Tips
Networking: Join Communities such as Linked In, and LUX academy to connect with fellow colleagues and professionals.
Resume and portfolio: Build your portfolio showcasing your projects and code on platforms such as Github, Personal website, or even X.
Job platforms: Use job searching platforms such as Linked in.
Prepare for technical interviews by practicing coding problems and case studies.
Conclusion
There is an increased demand for data professionals due an increase in the volume of data. The perfect time to begin your data career is now. Remember every data expert was once a beginner just like you.
A journey of thousands of steps begins with a single step.
