Data Science For Beginners 2023/2024

With the high rise of businesses in recent years especially embracing digitization, there is the need for organizations to leverage on statistical interpretation and data visualization for competitive advantage.

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 through your journey as a Data Scientist.

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 include;

1. Understanding The Problem
2. Gathering Data
3. Cleaning Data
4. Exploring Data
5. Data Drift and Model Analysis
6. Data Visualization
7. Interpreting data https://www.upgrad.com/blog/data-science-life-cycle/

Data Cleaning

Preprocess, and wrangle data. Pandas is 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.
Start with scikit-learn, a popular Python library for machine learning. Also exploratory data analysis (EDA) techniques help to gain insights from data.Use libraries like pandas,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:
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Common libraries and tools used in data science,as Jupyter notebooks and PyTorch for deep learning are effective tools to 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

At the end of this course, your knowledge on Data Science would have improved more on understanding a beginner's guide on Data Science, how Businesses must leverage on statistical interpretation as data science,career path in data science and the life cycle of data science.