Free AI Course from Google: https://ai.google/education/
Free Python IBM course with Certificate: https://cognitiveclass.ai/courses/python-for-data-science
Free Python IBM course with Certificate: https://cognitiveclass.ai/courses/python-for-data-science
Here is the list of few projects (found on kaggle). They cover Basics of Python, Advanced Statistics, Supervised Learning (Regression and Classification problems) & Data Science
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://news.1rj.ru/str/sqlproject
ENJOY LEARNING 👍👍
Please also check the discussions and notebook submissions for different approaches and solution after you tried yourself.
1. Basic python and statistics
Pima Indians :- https://www.kaggle.com/uciml/pima-indians-diabetes-database
Cardio Goodness fit :- https://www.kaggle.com/saurav9786/cardiogoodfitness
Automobile :- https://www.kaggle.com/toramky/automobile-dataset
2. Advanced Statistics
Game of Thrones:-https://www.kaggle.com/mylesoneill/game-of-thrones
World University Ranking:-https://www.kaggle.com/mylesoneill/world-university-rankings
IMDB Movie Dataset:- https://www.kaggle.com/carolzhangdc/imdb-5000-movie-dataset
3. Supervised Learning
a) Regression Problems
How much did it rain :- https://www.kaggle.com/c/how-much-did-it-rain-ii/overview
Inventory Demand:- https://www.kaggle.com/c/grupo-bimbo-inventory-demand
Property Inspection predictiion:- https://www.kaggle.com/c/liberty-mutual-group-property-inspection-prediction
Restaurant Revenue prediction:- https://www.kaggle.com/c/restaurant-revenue-prediction/data
IMDB Box office Prediction:-https://www.kaggle.com/c/tmdb-box-office-prediction/overview
b) Classification problems
Employee Access challenge :- https://www.kaggle.com/c/amazon-employee-access-challenge/overview
Titanic :- https://www.kaggle.com/c/titanic
San Francisco crime:- https://www.kaggle.com/c/sf-crime
Customer satisfcation:-https://www.kaggle.com/c/santander-customer-satisfaction
Trip type classification:- https://www.kaggle.com/c/walmart-recruiting-trip-type-classification
Categorize cusine:- https://www.kaggle.com/c/whats-cooking
4. Some helpful Data science projects for beginners
https://www.kaggle.com/c/house-prices-advanced-regression-techniques
https://www.kaggle.com/c/digit-recognizer
https://www.kaggle.com/c/titanic
5. Intermediate Level Data science Projects
Black Friday Data : https://www.kaggle.com/sdolezel/black-friday
Human Activity Recognition Data : https://www.kaggle.com/uciml/human-activity-recognition-with-smartphones
Trip History Data : https://www.kaggle.com/pronto/cycle-share-dataset
Million Song Data : https://www.kaggle.com/c/msdchallenge
Census Income Data : https://www.kaggle.com/c/census-income/data
Movie Lens Data : https://www.kaggle.com/grouplens/movielens-20m-dataset
Twitter Classification Data : https://www.kaggle.com/c/twitter-sentiment-analysis2
Share with credits: https://news.1rj.ru/str/sqlproject
ENJOY LEARNING 👍👍
❤6👍1
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https://news.1rj.ru/str/getjobss/1394
Required Technical and Professional Expertise
👉 Minimum 0-1 year of experience in Know your customer, Customer due diligence & Customer identification program (KYC/CDD/EDD/CIP)
👉 Working knowledge in US/UK regulatory policies
👉 Proven ability to deal with highly personal, confidential information.
👉 Experience in financial services transaction data analysis
👉 Excellent English writing, reading and speaking skill.
👉 Good analytical and problem-solving skills.
Important tools & programming languages required to become a data analyst
As a data analyst, there are several tools available that can greatly assist you in your work. Here are some commonly used tools for data analysts:
👉 Microsoft Excel: Excel is a widely used spreadsheet software that offers powerful data analysis capabilities. It allows you to organize and manipulate data, perform calculations, create charts and graphs, and conduct basic data analysis.
👉 SQL (Structured Query Language): SQL is a programming language used for managing and manipulating relational databases. It allows data analysts to extract, transform, and analyze data stored in databases. Popular database management systems that use SQL include MySQL, PostgreSQL, and Microsoft SQL Server.
👉 Python: Python is a versatile programming language that is popular among data analysts. It offers a wide range of libraries and frameworks specifically designed for data analysis, such as NumPy, Pandas, and Matplotlib. Python allows you to perform advanced data manipulation, statistical analysis, data visualization, and machine learning.
👉 R: R is another programming language commonly used for statistical computing and data analysis. It provides a comprehensive set of packages and libraries tailored for data analysis tasks. R is especially popular for statistical modeling, data visualization, and exploratory data analysis.
👉 Tableau: Tableau is a powerful data visualization tool that enables data analysts to create interactive and visually appealing dashboards and reports. It allows you to connect to various data sources, perform data blending, and present insights in a user-friendly manner.
👉 Power BI: Power BI is a business intelligence tool developed by Microsoft. It provides data visualization capabilities, data modeling, and interactive dashboards. Power BI allows data analysts to create compelling visualizations, collaborate with others, and share insights across organizations.
👉 Apache Spark: Apache Spark is an open-source big data processing framework that enables data analysts to handle large-scale datasets and perform distributed data processing. It provides efficient data querying, machine learning, and real-time data streaming capabilities.
These are just a few examples of the many tools available to data analysts. The choice of tools depends on the specific requirements of your projects, your technical skills, and the preferences of your organization. It's beneficial to have a solid understanding of a combination of these tools to enhance your data analysis capabilities.
Data Analyst Roadmap for Beginners
https://news.1rj.ru/str/learndataanalysis/112
ENJOY LEARNING 👍👍
As a data analyst, there are several tools available that can greatly assist you in your work. Here are some commonly used tools for data analysts:
👉 Microsoft Excel: Excel is a widely used spreadsheet software that offers powerful data analysis capabilities. It allows you to organize and manipulate data, perform calculations, create charts and graphs, and conduct basic data analysis.
👉 SQL (Structured Query Language): SQL is a programming language used for managing and manipulating relational databases. It allows data analysts to extract, transform, and analyze data stored in databases. Popular database management systems that use SQL include MySQL, PostgreSQL, and Microsoft SQL Server.
👉 Python: Python is a versatile programming language that is popular among data analysts. It offers a wide range of libraries and frameworks specifically designed for data analysis, such as NumPy, Pandas, and Matplotlib. Python allows you to perform advanced data manipulation, statistical analysis, data visualization, and machine learning.
👉 R: R is another programming language commonly used for statistical computing and data analysis. It provides a comprehensive set of packages and libraries tailored for data analysis tasks. R is especially popular for statistical modeling, data visualization, and exploratory data analysis.
👉 Tableau: Tableau is a powerful data visualization tool that enables data analysts to create interactive and visually appealing dashboards and reports. It allows you to connect to various data sources, perform data blending, and present insights in a user-friendly manner.
👉 Power BI: Power BI is a business intelligence tool developed by Microsoft. It provides data visualization capabilities, data modeling, and interactive dashboards. Power BI allows data analysts to create compelling visualizations, collaborate with others, and share insights across organizations.
👉 Apache Spark: Apache Spark is an open-source big data processing framework that enables data analysts to handle large-scale datasets and perform distributed data processing. It provides efficient data querying, machine learning, and real-time data streaming capabilities.
These are just a few examples of the many tools available to data analysts. The choice of tools depends on the specific requirements of your projects, your technical skills, and the preferences of your organization. It's beneficial to have a solid understanding of a combination of these tools to enhance your data analysis capabilities.
Data Analyst Roadmap for Beginners
https://news.1rj.ru/str/learndataanalysis/112
ENJOY LEARNING 👍👍
👍5
Free courses from Microsoft ✅
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1. HTML
html.com
2. CSS
css-tricks.com
3. JavaScript
javanoscript.info
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git-scm.com
6. React
react-tutorial.app
7. APIs
rapidapi.com
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sqlbolt.com
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https://news.1rj.ru/str/getjobss/1413
Required Skills:
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👉 3+ yr of experience as Business Analyst
👉 Excellent planning, organizational, and time management skills
👉Strong problem solving & ability to work in ambiguous environments with high ownership
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https://news.1rj.ru/str/datasciencefun/1142
👉 Frontend development
https://i0.wp.com/css-tricks.com/wp-content/uploads/2018/07/modern-front-end-developer.png?ssl=1
https://news.1rj.ru/str/webdevcoursefree/432
👉 AI/ML
https://i.am.ai/roadmap
Join @free4unow_backup for more free courses
ENJOY LEARNING 👍👍
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Important Pandas topics for a data analysis interviews
👉 DataFrame and Series: Understand the fundamental data structures in pandas. A DataFrame is a 2-dimensional labeled data structure, while a Series is a 1-dimensional labeled array.
👉 Data Cleaning and Manipulation: Be able to clean and preprocess data using functions like drop, fillna, replace, and apply. Know how to filter and select specific rows and columns using conditions.
👉 Indexing and Slicing: Understand how to use various indexing techniques like label-based indexing (loc) and position-based indexing (iloc). Practice slicing data for specific rows and columns.
👉 Grouping and Aggregation: Know how to use the groupby function to group data based on certain columns and perform aggregation functions like sum, mean, count, etc.
👉 Merging and Joining: Be familiar with methods to combine multiple DataFrames using merge and join operations. Understand the different types of joins (inner, outer, left, right) and when to use them.
👉 Reshaping Data: Learn about techniques to reshape data using functions like pivot, melt, and stack/unstack. Understand the concept of wide and long data formats.
👉 Data Visualization: While not exclusive to pandas, you might need to use pandas to prepare data for visualization. Familiarize yourself with plotting functions and libraries like Matplotlib and Seaborn.
👉 Handling Dates and Time: Be comfortable working with date and time data using pandas' datetime functionality. This includes date parsing, date arithmetic, and resampling time series data.
👉 Handling Missing Data: Learn techniques to identify and handle missing data, such as using functions like isna, fillna, and considering strategies for imputation.
👉 Performance Optimization: Understand ways to optimize performance when working with large datasets, such as using vectorized operations and avoiding unnecessary loops.
👉 Reading and Writing Data: Know how to read data from various file formats (CSV, Excel, SQL databases) into pandas DataFrames and write DataFrame data back to these formats.
👉 Exploratory Data Analysis (EDA): Practice using pandas to perform basic exploratory data analysis tasks like summarizing data, calculating basic statistics, and identifying trends or patterns.
Free Resources to learn Pandas
👇👇
https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
https://news.1rj.ru/str/DataAnalystInterview/55
https://bit.ly/3LkLtLj
https://bit.ly/3DFMgDY
https://news.1rj.ru/str/learndataanalysis/30
Remember, the depth of your understanding in each topic will depend on the specific requirements of the interview and the role you're applying for. Practice by working on real datasets and solving data analysis problems using pandas to build your proficiency in these areas.
ENJOY LEARNING 👍👍
👉 DataFrame and Series: Understand the fundamental data structures in pandas. A DataFrame is a 2-dimensional labeled data structure, while a Series is a 1-dimensional labeled array.
👉 Data Cleaning and Manipulation: Be able to clean and preprocess data using functions like drop, fillna, replace, and apply. Know how to filter and select specific rows and columns using conditions.
👉 Indexing and Slicing: Understand how to use various indexing techniques like label-based indexing (loc) and position-based indexing (iloc). Practice slicing data for specific rows and columns.
👉 Grouping and Aggregation: Know how to use the groupby function to group data based on certain columns and perform aggregation functions like sum, mean, count, etc.
👉 Merging and Joining: Be familiar with methods to combine multiple DataFrames using merge and join operations. Understand the different types of joins (inner, outer, left, right) and when to use them.
👉 Reshaping Data: Learn about techniques to reshape data using functions like pivot, melt, and stack/unstack. Understand the concept of wide and long data formats.
👉 Data Visualization: While not exclusive to pandas, you might need to use pandas to prepare data for visualization. Familiarize yourself with plotting functions and libraries like Matplotlib and Seaborn.
👉 Handling Dates and Time: Be comfortable working with date and time data using pandas' datetime functionality. This includes date parsing, date arithmetic, and resampling time series data.
👉 Handling Missing Data: Learn techniques to identify and handle missing data, such as using functions like isna, fillna, and considering strategies for imputation.
👉 Performance Optimization: Understand ways to optimize performance when working with large datasets, such as using vectorized operations and avoiding unnecessary loops.
👉 Reading and Writing Data: Know how to read data from various file formats (CSV, Excel, SQL databases) into pandas DataFrames and write DataFrame data back to these formats.
👉 Exploratory Data Analysis (EDA): Practice using pandas to perform basic exploratory data analysis tasks like summarizing data, calculating basic statistics, and identifying trends or patterns.
Free Resources to learn Pandas
👇👇
https://www.freecodecamp.org/learn/data-analysis-with-python/#data-analysis-with-python-course
https://news.1rj.ru/str/DataAnalystInterview/55
https://bit.ly/3LkLtLj
https://bit.ly/3DFMgDY
https://news.1rj.ru/str/learndataanalysis/30
Remember, the depth of your understanding in each topic will depend on the specific requirements of the interview and the role you're applying for. Practice by working on real datasets and solving data analysis problems using pandas to build your proficiency in these areas.
ENJOY LEARNING 👍👍
👍8
Learn for free ✅
HTML — https://www.w3schools.com/html
CSS — http://CSS-tricks.com
JavaScript — http://LearnJavaScript.online
DSA --- https://news.1rj.ru/str/dsabooks/21
Git, GitHub -- http://LearnGitBranching.js.org
React — http://React-tutorial.app
API — http://RapidAPI.com/comics
SQL — http://SQLbolt.com
Python -- https://news.1rj.ru/str/pythondevelopersindia/76
PHP --- https://bit.ly/3QkY3wW
ML -- https://developers.google.com/machine-learning/crash-course
AI -- http://microsoft.github.io/AI-For-Beginners
ENJOY LEARNING 👍👍
HTML — https://www.w3schools.com/html
CSS — http://CSS-tricks.com
JavaScript — http://LearnJavaScript.online
DSA --- https://news.1rj.ru/str/dsabooks/21
Git, GitHub -- http://LearnGitBranching.js.org
React — http://React-tutorial.app
API — http://RapidAPI.com/comics
SQL — http://SQLbolt.com
Python -- https://news.1rj.ru/str/pythondevelopersindia/76
PHP --- https://bit.ly/3QkY3wW
ML -- https://developers.google.com/machine-learning/crash-course
AI -- http://microsoft.github.io/AI-For-Beginners
ENJOY LEARNING 👍👍
Channels that you MUST follow in 2023:
✅ @getjobss - Jobs and Internship Opportunities
✅ @englishlearnerspro - improve your English
✅ @datasciencefun - Learn Data Science and Machibe Learning
✅ @crackingthecodinginterview - boost your coding knowledge
✅ @learndataanalysis - Data Analysis Books
✅ @programming_guide - Coding Books
✅ @getjobss - Jobs and Internship Opportunities
✅ @englishlearnerspro - improve your English
✅ @datasciencefun - Learn Data Science and Machibe Learning
✅ @crackingthecodinginterview - boost your coding knowledge
✅ @learndataanalysis - Data Analysis Books
✅ @programming_guide - Coding Books
👍2
🔟 Web development project ideas for beginners
Personal Portfolio Website: Create a website showcasing your skills, projects, and resume. This will help you practice HTML, CSS, and potentially some JavaScript for interactivity.
To-Do List App: Build a simple to-do list application using HTML, CSS, and JavaScript. You can gradually enhance it by adding features like task priority, due dates, and local storage.
Blog Platform: Create a basic blog platform where users can create, edit, and delete posts. This will give you experience with user authentication, databases, and CRUD operations.
E-commerce Website: Design a mock e-commerce site to learn about product listings, shopping carts, and checkout processes. This project will introduce you to handling user input and creating dynamic content.
Weather App: Develop a weather app that fetches data from a weather API and displays current conditions and forecasts. This project will involve API integration and working with JSON data.
Recipe Sharing Site: Build a platform where users can share and browse recipes. You can implement search functionality and user authentication to enhance the project.
Social Media Dashboard: Create a simplified social media dashboard that displays metrics like followers, likes, and comments. This project will help you practice data visualization and working with APIs.
Online Quiz App: Develop an online quiz application that lets users take quizzes on various topics. You can include features like multiple-choice questions, timers, and score tracking.
Personal Blog: Start your own blog by developing a content management system (CMS) where you can create, edit, and publish articles. This will give you hands-on experience with database management.
Event Countdown Timer: Build a countdown timer for upcoming events. You can make it interactive by allowing users to set their own event names and dates.
Remember, the key is to start small and gradually add complexity to your projects as you become more comfortable with different technologies concepts. These projects will not only showcase your skills to potential employers but also help you learn and grow as a web developer.
Free Resources to learn web development https://news.1rj.ru/str/free4unow_backup/554
ENJOY LEARNING 👍👍
Personal Portfolio Website: Create a website showcasing your skills, projects, and resume. This will help you practice HTML, CSS, and potentially some JavaScript for interactivity.
To-Do List App: Build a simple to-do list application using HTML, CSS, and JavaScript. You can gradually enhance it by adding features like task priority, due dates, and local storage.
Blog Platform: Create a basic blog platform where users can create, edit, and delete posts. This will give you experience with user authentication, databases, and CRUD operations.
E-commerce Website: Design a mock e-commerce site to learn about product listings, shopping carts, and checkout processes. This project will introduce you to handling user input and creating dynamic content.
Weather App: Develop a weather app that fetches data from a weather API and displays current conditions and forecasts. This project will involve API integration and working with JSON data.
Recipe Sharing Site: Build a platform where users can share and browse recipes. You can implement search functionality and user authentication to enhance the project.
Social Media Dashboard: Create a simplified social media dashboard that displays metrics like followers, likes, and comments. This project will help you practice data visualization and working with APIs.
Online Quiz App: Develop an online quiz application that lets users take quizzes on various topics. You can include features like multiple-choice questions, timers, and score tracking.
Personal Blog: Start your own blog by developing a content management system (CMS) where you can create, edit, and publish articles. This will give you hands-on experience with database management.
Event Countdown Timer: Build a countdown timer for upcoming events. You can make it interactive by allowing users to set their own event names and dates.
Remember, the key is to start small and gradually add complexity to your projects as you become more comfortable with different technologies concepts. These projects will not only showcase your skills to potential employers but also help you learn and grow as a web developer.
Free Resources to learn web development https://news.1rj.ru/str/free4unow_backup/554
ENJOY LEARNING 👍👍
❤3👍1
🔟 Data Science Project Ideas for Freshers
Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.
Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.
Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.
Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.
Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.
Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).
Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.
Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.
Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.
A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.
Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.
Free datasets to build the projects
👇👇
https://news.1rj.ru/str/datasciencefun/1126
ENJOY LEARNING 👍👍
Exploratory Data Analysis (EDA) on a Dataset: Choose a dataset of interest and perform thorough EDA to extract insights, visualize trends, and identify patterns.
Predictive Modeling: Build a simple predictive model, such as linear regression, to predict a target variable based on input features. Use libraries like scikit-learn to implement the model.
Classification Problem: Work on a classification task using algorithms like decision trees, random forests, or support vector machines. It could involve classifying emails as spam or not spam, or predicting customer churn.
Time Series Analysis: Analyze time-dependent data, like stock prices or temperature readings, to forecast future values using techniques like ARIMA or LSTM.
Image Classification: Use convolutional neural networks (CNNs) to build an image classification model, perhaps classifying different types of objects or animals.
Natural Language Processing (NLP): Create a sentiment analysis model that classifies text as positive, negative, or neutral, or build a text generator using recurrent neural networks (RNNs).
Clustering Analysis: Apply clustering algorithms like k-means to group similar data points together, such as segmenting customers based on purchasing behaviour.
Recommendation System: Develop a recommendation engine using collaborative filtering techniques to suggest products or content to users.
Anomaly Detection: Build a model to detect anomalies in data, which could be useful for fraud detection or identifying defects in manufacturing processes.
A/B Testing: Design and analyze an A/B test to compare the effectiveness of two different versions of a web page or app feature.
Remember to document your process, explain your methodology, and showcase your projects on platforms like GitHub or a personal portfolio website.
Free datasets to build the projects
👇👇
https://news.1rj.ru/str/datasciencefun/1126
ENJOY LEARNING 👍👍
👍5
🔟 Project Ideas for a data analyst
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://news.1rj.ru/str/sqlspecialist/379
ENJOY LEARNING 👍👍
Customer Segmentation: Analyze customer data to segment them based on their behaviors, preferences, or demographics, helping businesses tailor their marketing strategies.
Churn Prediction: Build a model to predict customer churn, identifying factors that contribute to churn and proposing strategies to retain customers.
Sales Forecasting: Use historical sales data to create a predictive model that forecasts future sales, aiding inventory management and resource planning.
Market Basket Analysis: Analyze
transaction data to identify associations between products often purchased together, assisting retailers in optimizing product placement and cross-selling.
Sentiment Analysis: Analyze social media or customer reviews to gauge public sentiment about a product or service, providing valuable insights for brand reputation management.
Healthcare Analytics: Examine medical records to identify trends, patterns, or correlations in patient data, aiding in disease prediction, treatment optimization, and resource allocation.
Financial Fraud Detection: Develop algorithms to detect anomalous transactions and patterns in financial data, helping prevent fraud and secure transactions.
A/B Testing Analysis: Evaluate the results of A/B tests to determine the effectiveness of different strategies or changes on websites, apps, or marketing campaigns.
Energy Consumption Analysis: Analyze energy usage data to identify patterns and inefficiencies, suggesting strategies for optimizing energy consumption in buildings or industries.
Real Estate Market Analysis: Study housing market data to identify trends in property prices, rental rates, and demand, assisting buyers, sellers, and investors in making informed decisions.
Remember to choose a project that aligns with your interests and the domain you're passionate about.
Data Analyst Roadmap
👇👇
https://news.1rj.ru/str/sqlspecialist/379
ENJOY LEARNING 👍👍
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