Data Science: Data science is an interdisciplinary field that uses scientific methods, algorithms, combines expertise in statistics, programming, domain knowledge, and data visualization to solve complex problems and make data-driven decisions. Data scientists collect, clean, analyze, and interpret data to uncover valuable information that can be used for various purposes, such as improving business strategies, making predictions, and understanding trends.
SQL (Structured Query Language)20 DAYS: SQL is a programming language used to manage and query databases. In data science, it's vital for tasks like retrieving specific data, cleaning and transforming data, and integrating data from various sources. SQL is essential for structured data analysis.
a) Tutorials - https://lnkd.in/epAFJzJB
b) Projects - https://lnkd.in/eq9jqcBq
Python 20 DAYS: Python is a versatile programming language widely used in data science. It offers libraries like Pandas and NumPy for data manipulation, scikit-learn, TensorFlow, and PyTorch for machine learning, and Matplotlib and Seaborn for data visualization. Python integrates well with SQL for data analysis, making it popular among data scientists.
a) Tutorials - https://lnkd.in/eh4gTQQ2
b) Projects-https://lnkd.in/emzcrzTX
Power BI: Power BI, developed by Microsoft, is a tool for data visualization, reporting, and interactive dashboards. Data scientists use it to create engaging reports and dashboards that communicate data insights effectively. Power BI connects to different data sources, simplifies data transformation, and enables easy sharing of reports with stakeholders.
Create a comprehensive data science portfolio over the next 6-12 months by working on a variety of projects from different sources, including Kaggle competitions and personal initiatives.
In Conclusion, data science is the interdisciplinary field of extracting insights from data. SQL is used for managing and querying databases, Python is a versatile language for data tasks, and Power BI is for creating impactful data visualizations and reports. These tools are frequently used together by data scientists to extract, analyze, and communicate insights from data.
Top comments (0)