Introduction
With the high rise of businesses in recent years especially embracing digitization, there is a need for organizations to leverage statistical interpretation and data visualization for competitive advantage.
Understanding Data Science
Data science is a multidisciplinary field that involves the use of different techniques, algorithms, processes, and systems to extract insights from data. It combines elements from statistics, computer science, and domain expertise to analyze and interpret data. If you're a beginner interested in learning data science, here's a step-by-step guide to get you started:
Data Science Basics
Basic fundamental concepts like data information including data collection, preprocessing, analysis, and interpretation are key steps to effectively guide you through your journey as a beginner in Data Science.
Programing Language You Will Need
Start by learning Python basics as Python is the basic language for learning data science. Libraries like NumPy, pandas, and matplotlib for data manipulation and visualization.
Data Science Life Cycle
Data Science life cycle involves different repeated activities a data Scientist must follow. This includes;
1. Understanding The Problem
2. Gathering Data
3. Cleaning Data
4. Exploring Data
5. Data Drift and Model Analysis
5. Data Visualization
6. Interpreting data
Data Cleaning
Preprocess, and wrangle data. Pandas are an essential library for this purpose.
Data Visualization:
Master data visualization tools like Matplotlib and Pandas to create meaningful plots and charts.
Machine Learning:
With the basics of machine learning, including supervised and unsupervised learning. An exploratory data analysis (EDA) technique can help to gain insights from data using libraries like pandas, and NumPy for EDA.
SQL is a valuable skill for querying relational databases. Learning how to work with databases, as data is often stored in databases. Exploring big data technologies like Hadoop and Spark if you plan to work with large datasets is a good start.
Data Science Libraries and Tools:
Common libraries and tools used in data science, such as Jupyter Notebooks and PyTorch for deep learning are effective tools for achieving projects in data science.
Career Path For Data Science
1. Data Analyst
2. Data Scientist
3. Data Engineer
4. BI Analyst
5. Machine Learning Engineer (designing Machine Learning algorithm)
6. NLP Engineer
Conclusion
Having understood what Data Science is from the article above, businesses can leverage statistical interpretation as data analytics and science to enable ease of business insight. As a beginner, you can learn new techniques, tools, one or two programming languages and libraries in data science and data science life cycle to help you navigate in your career journey.