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Data Science Consultant — YuvaIntern

Data Science Consultant

Duration: 8 Weeks Mode: Virtual Data scientist internship
Apply for Internship
Yuva Intern Offer Letter
Step 1: Apply for your favorite Internship

After you apply, you will receive an offer letter instantly. No queues, no uncertainty—just a quick start to your career journey.

Yuva Intern Task
Step 2: Submit Your Task(s)

You will be assigned weekly tasks to complete. Submit them on time to earn your certificate.

Yuva Intern Evaluation
Step 3: Your task(s) will be evaluated

Your tasks will be evaluated by our team. You will receive feedback and suggestions for improvement.

Yuva Intern Certificate
Step 4: Receive your Certificate

Once you complete your tasks, you will receive a certificate of completion. This certificate will be a valuable addition to your resume.

About this Internship

Consult on data strategies, analytics solutions, and help organizations leverage data for growth.

Tasks and Duties
1

Data Science Fundamentals Assessment

Mandatory Weightage: 100%

Summary

Apply Python, statistics, data types, and data analysis fundamentals to explore a real-world dataset and generate meaningful insights.

Objective

1)

Understand the Dataset — Select a real-world dataset and identify its columns, data types, structure, and business context.

2)

Apply Python Fundamentals — Use Python data structures, functions, loops, conditional statements, and libraries such as Pandas and NumPy for data analysis.

3)

Perform Data Cleaning — Handle missing values, duplicate records, inconsistent data, and incorrect data types to prepare the dataset for analysis.

4)

Apply Statistical Concepts — Calculate and interpret measures such as mean, median, mode, standard deviation, variance, and basic correlations.

5)

Perform Exploratory Data Analysis — Analyze patterns, trends, distributions, relationships, and outliers using suitable tables and visualizations.

6)

Prepare an Analysis Report — Document the methodology, data preparation, statistical analysis, visualizations, key insights, and conclusions following data science best practices.

2

Hands-On Data Lab Implementation

Mandatory Weightage: 100%

Summary

Set up a Python data science environment and apply practical skills in data manipulation, visualization, statistical analysis, and exploratory data analysis.

Objective

1)

Set Up the Data Science Environment — Install and configure Python, Jupyter Notebook, and essential libraries such as Pandas, NumPy, Matplotlib, and Seaborn.

2)

Import & Explore Data — Load a dataset into Jupyter and examine its structure, columns, data types, missing values, and basic statistics.

3)

Perform Data Manipulation — Practice filtering, sorting, selecting, grouping, merging, and transforming data using Pandas.

4)

Clean the Dataset — Handle missing values, duplicates, inconsistent data, and incorrect data types to prepare the data for analysis.

5)

Create Data Visualizations — Develop suitable charts such as bar charts, histograms, line charts, scatter plots, and box plots to identify patterns and trends.

6)

Perform Basic Data Analysis — Apply descriptive statistics and exploratory analysis to generate meaningful insights and document the observations in a Jupyter Notebook.

3

Real-World Dataset Analysis Project

Mandatory Weightage: 100%

Summary

Analyze a real-world dataset by performing data cleaning, preprocessing, exploratory analysis, visualization, and interpretation to generate meaningful insights.

Objective

1)

Select a Real-World Dataset — Choose a relevant dataset from a reliable source and understand its business or analytical context.

2)

Explore the Dataset — Examine data types, structure, distributions, missing values, duplicates, and relationships between variables.

3)

Clean & Preprocess Data — Handle missing values, duplicates, inconsistent records, outliers, and incorrect data types using appropriate techniques.

4)

Perform Exploratory Data Analysis — Use Pandas, NumPy, and statistical methods to identify important patterns, trends, correlations, and observations.

5)

Create Data Visualizations — Build meaningful charts such as bar charts, histograms, line charts, scatter plots, and box plots to communicate findings effectively.

6)

Present Insights & Interpretations — Prepare a Jupyter Notebook or analysis report explaining the methodology, visualizations, key findings, interpretations, and conclusions.

4

Data Science Tool Mastery Project

Mandatory Weightage: 100%

Summary

Become proficient with key data science tools and libraries

Objective

1)

Master Python & Data Science Libraries — Develop proficiency in Python, NumPy, Pandas, Matplotlib, and Seaborn.

2)

Practice Data Analysis — Use Pandas and NumPy for data cleaning, transformation, aggregation, and analysis.

3)

Build Data Visualizations — Create meaningful visualizations using Matplotlib and Seaborn.

4)

Apply Machine Learning — Use Scikit-learn for preprocessing, model building, evaluation, and interpretation.

5)

Work with SQL & BI Tools — Analyze structured data using SQL and create business dashboards using Power BI.

6)

Create Portfolio Projects — Use Jupyter Notebook and Git/GitHub to document and showcase practical Data Science projects.

5

Analytics Report & Insights Documentation

Mandatory Weightage: 100%

Summary

Create a comprehensive analytics report with executive summary, findings, visualizations, insights, and actionable recommendations for business stakeholders.

Objective

1)

Define Business Objectives — Understand the business problem, reporting goals, key questions, and stakeholder requirements.

2)

Analyze Data & Identify Findings — Examine relevant data to identify important trends, patterns, relationships, and performance indicators.

3)

Create Meaningful Visualizations — Develop clear charts, graphs, dashboards, and tables that effectively communicate key findings.

4)

Generate Business Insights — Interpret analytical results and explain their potential impact on business performance and decision-making.

5)

Prepare Executive Summary — Summarize the most important findings, insights, metrics, and business implications in a stakeholder-friendly format.

6)

Provide Actionable Recommendations — Develop practical, data-driven recommendations and clearly document next steps for business stakeholders.

6

Final Data Science Capstone Project

Mandatory Weightage: 100%

Summary

Complete an end-to-end Data Science capstone demonstrating data analysis, visualization, machine learning, interpretation, and business or research insights.

Objective

1)

Define the Capstone Problem — Select a real-world business, analytical, or research problem and clearly define its objectives, scope, and expected outcomes.

2)

Collect & Prepare Data — Gather relevant data, perform data cleaning, preprocessing, feature engineering, and exploratory data analysis.

3)

Apply Data Science Techniques — Use appropriate statistical methods, machine learning algorithms, or analytical techniques to solve the selected problem.

4)

Evaluate & Interpret Results — Evaluate the solution using suitable metrics and interpret the results to identify meaningful patterns, insights, and limitations.

5)

Create Visualizations & Recommendations — Develop clear visualizations and translate findings into practical, data-driven recommendations or conclusions.

6)

Present the Final Capstone — Prepare a complete project report and presentation covering the problem, methodology, data preparation, analysis/model, results, insights, recommendations, and future scope.