Data Science
The primary goal of data science is to uncover patterns, make predictions, and derive actionable insights from data to solve real-world problems and drive informed decision-making. Data scientists use a combination of statistical analysis, machine learning algorithms, data visualization, and data engineering techniques to extract valuable information from data. In this course, you will be using technologies such as microsoft excel, R, Python, pandas, numpy, Power BI to perform different aspect of data analysis.
Data Collection
Data scientists gather and acquire relevant data from various sources, including databases, APIs, websites, and sensors. This involves understanding the data requirements, identifying data sources, and developing strategies for data collection.
Data Cleaning And Preprocessing
Raw data often contains errors, inconsistencies, missing values, and outliers. Data scientists apply techniques to clean and preprocess the data, including handling missing values, removing outliers, and transforming the data into a suitable format for analysis.
Exploratory Data Analysis(EDA)
EDA involves examining and visualizing the data to gain insights and understand the underlying patterns. Data scientists use various statistical techniques and data visualization tools to explore relationships, distributions, and trends in the data.
Data Visualization
Data scientists use visualizations to present findings and communicate insights effectively. Data visualization techniques help in understanding complex data patterns, trends, and relationships, making it easier for stakeholders to grasp and interpret the information
