This is a 8-week hands-on internship programme offered by PrepInsta. In this duration, we will learn and implement the skills to our projects. During this 8 weeks, we will learn Excel, Python, Tableau, SQL, Kaggle Notebooks, conditional statements, loops, libraries like Numpy, Pandas, Matplotlib, Seaborn, Plotly, etc. We will also be doing Web Scraping, data cleaning, EDA, data collection,data vizualization, dashboard planning, and many more just to name a few.
Create a functional dashboard in Google Sheets/Excel to analyze and visualize data related to bike buyers. Your dashboard should provide insights into the behavior and preferences of bike buyers.
The Frog and Leap game is a classic puzzle game where we have to switch the positions of the green and brown frogs to win the game.
RULES OF THE GAME:-
- The left set of frogs can only move right, the right set of frogs can only move left.
- Frogs can move forward one space, or move two spaces by jumping over another frog from opposite side.
- The puzzle is solved when the two sets of frogs have switched positions sucessfully.
Reference:- Link to the game
- Handle missing values and any duplicate records in the dataset and correct any inconsistencies or errors in data entry.
- Transforming the dataset using pandas and numpy according to analysis requirement. Consider creating new features, normalizing data, or handling categorical variables.
- Identify and address outliers in the dataset using the Numpy and Pandas methods. Decide on an appropriate approach, whether it’s removing outliers or transforming them.
- Validate the cleaned and transformed dataset to ensure that it aligns with the intended analysis goals. Perform sanity checks on key variables and relationships within the data.
Using Python's built in data analysis and graph libraries like Plotly, Matplotlib and Seaborn, show the insights/patterns that can be generated using the given dataset.
Solving a murder mystery using SQL (MySQL, Oracle, SQLite, etc). Develop SQL queries to extract relevant information from the database. Use SELECT statements, JOIN operations, and WHERE clauses to uncover clues and solve the murder mystery.
Select a target website and use web scraping libraries like BeautifulSoup and Requests and extract relevant data from the website and store it in a suitable format for analysis.
The goal is to perform tasks such as data profiling, missing data handling, and visual exploration to uncover insights and patterns within the data.
- Dataset Selection: Choose a real-world dataset for analysis. It could be related to any field of interest—healthcare, finance, social sciences, etc.
- Data Profiling: Conduct data profiling to gain an initial understanding of the dataset’s structure. Examine the data types, unique values, and basic statistics of each column.
- Missing Data Handling: Identify and handle missing data in the dataset. Employ strategies such as imputation or removal of missing values based on the nature of the data.
- Data Cleaning: Clean the dataset by addressing any inconsistencies, errors, or outliers.
- Descriptive Statistics: Calculate and present descriptive statistics for key variables in the dataset. Utilize statistical measures to describe the central tendency, dispersion, and shape of the data.
- Correlation Analysis: Perform correlation analysis to identify relationships between variables. Interpret the correlation coefficients and understand the implications for the dataset.
- Visual Exploration: Create visualizations to explore relationships and patterns within the data. Utilise charts, graphs, and other visual tools to represent the dataset’s characteristics.
Design and build a Tableau dashboard to visually present data insights and key findings from a provided dataset. Your goal is to create an interactive and informative dashboard that effectively communicates the story within the data.
- Map Visualization:
- Use geographical maps to visualize air quality levels across different states or regions in India.
- Color code the map markers based on the air quality index (AQI) or other relevant metrics.
- Time Series Charts:
- Create time series line charts to depict changes in air quality over the years.
- Group the data by months or years to identify patterns and trends.
- Policy Change Timeline:
- Integrate a timeline component to showcase key environmental policy changes in India.
- Highlight these policy change events on the timeline to observe correlations with air quality variations.
- Filter Options:
- Implement filters for users to select specific states, periods, or pollutant types.
- Allow users to customize their views based on their interests or research questions.
- Comparative Analysis:
- Include comparative charts to showcase air quality comparisons between different states or regions.
- Utilize bar charts or line charts to illustrate variations in air quality metrics.
- Top Pollutant Analysis:
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Identify and display the top pollutants contributing to poor air quality.
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Utilize bar charts or pie charts to represent the proportion of each pollutant.
Objective: Imagine you are a data analyst at “HealthTrackers Inc.,” a fictional company operating in the Fitbit industry. Your company is dedicated to understanding consumer behavior to enhance product offerings and optimize marketing strategies. You have been tasked with analyzing a comprehensive dataset obtained from Fitbit users to uncover trends and insights. The business objective is to identify key trends, understand their implications for customers, and leverage these insights to shape an effective marketing strategy.
Business Task: Analyze FitBit Fitness Tracker App data to gain insights into how consumers are using the FitBit app and discover trends and insights for the marketing team. Business Objectives:
- What are the trends identified?
- How could these trends apply to customers?
- How could these trends help influence marketing strategy?
Business Objectives:
- What are the trends identified?
- How could these trends apply to customers?
- How could these trends help influence marketing strategy?