Top 5 data science projects for freshers

1. Predictive Analytics on a Dataset:
   - Use a dataset to predict future trends or outcomes using machine learning algorithms. This could involve predicting sales, stock prices, or any other relevant domain.

2. Customer Segmentation:
   - Analyze and segment customers based on their behavior, preferences, or demographics. This project could provide insights for targeted marketing strategies.

3. Sentiment Analysis on Social Media Data:
   - Analyze sentiment in social media data to understand public opinion on a particular topic. This project helps in mastering natural language processing (NLP) techniques.

4. Recommendation System:
   - Build a recommendation system, perhaps for movies, music, or products, using collaborative filtering or content-based filtering methods.

5. Fraud Detection:
   - Develop a fraud detection system using machine learning algorithms to identify anomalous patterns in financial transactions or any domain where fraud detection is crucial.

Free Datsets -> https://t.iss.one/DataPortfolio/2?single

These projects showcase practical application of data science skills and can be highlighted on a resume for entry-level positions.

Join @pythonspecialist for more data science projects

Data Scientist Roadmap
|
|-- 1. Basic Foundations
|   |-- a. Mathematics
|   |   |-- i. Linear Algebra
|   |   |-- ii. Calculus
|   |   |-- iii. Probability
|   |   -- iv. Statistics
|   |
|   |-- b. Programming
|   |   |-- i. Python
|   |   |   |-- 1. Syntax and Basic Concepts
|   |   |   |-- 2. Data Structures
|   |   |   |-- 3. Control Structures
|   |   |   |-- 4. Functions
|   |   |   -- 5. Object-Oriented Programming
|   |   |
|   |   -- ii. R (optional, based on preference)
|   |
|   |-- c. Data Manipulation
|   |   |-- i. Numpy (Python)
|   |   |-- ii. Pandas (Python)
|   |   -- iii. Dplyr (R)
|   |
|   -- d. Data Visualization
|       |-- i. Matplotlib (Python)
|       |-- ii. Seaborn (Python)
|       -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
|   |-- a. Exploratory Data Analysis (EDA)
|   |-- b. Feature Engineering
|   |-- c. Data Cleaning
|   |-- d. Handling Missing Data
|   -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
|   |-- a. Supervised Learning
|   |   |-- i. Regression
|   |   |   |-- 1. Linear Regression
|   |   |   -- 2. Polynomial Regression
|   |   |
|   |   -- ii. Classification
|   |       |-- 1. Logistic Regression
|   |       |-- 2. k-Nearest Neighbors
|   |       |-- 3. Support Vector Machines
|   |       |-- 4. Decision Trees
|   |       -- 5. Random Forest
|   |
|   |-- b. Unsupervised Learning
|   |   |-- i. Clustering
|   |   |   |-- 1. K-means
|   |   |   |-- 2. DBSCAN
|   |   |   -- 3. Hierarchical Clustering
|   |   |
|   |   -- ii. Dimensionality Reduction
|   |       |-- 1. Principal Component Analysis (PCA)
|   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
|   |       -- 3. Linear Discriminant Analysis (LDA)
|   |
|   |-- c. Reinforcement Learning
|   |-- d. Model Evaluation and Validation
|   |   |-- i. Cross-validation
|   |   |-- ii. Hyperparameter Tuning
|   |   -- iii. Model Selection
|   |
|   -- e. ML Libraries and Frameworks
|       |-- i. Scikit-learn (Python)
|       |-- ii. TensorFlow (Python)
|       |-- iii. Keras (Python)
|       -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
|   |-- a. Neural Networks
|   |   |-- i. Perceptron
|   |   -- ii. Multi-Layer Perceptron
|   |
|   |-- b. Convolutional Neural Networks (CNNs)
|   |   |-- i. Image Classification
|   |   |-- ii. Object Detection
|   |   -- iii. Image Segmentation
|   |
|   |-- c. Recurrent Neural Networks (RNNs)
|   |   |-- i. Sequence-to-Sequence Models
|   |   |-- ii. Text Classification
|   |   -- iii. Sentiment Analysis
|   |
|   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
|   |   |-- i. Time Series Forecasting
|   |   -- ii. Language Modeling
|   |
|   -- e. Generative Adversarial Networks (GANs)
|       |-- i. Image Synthesis
|       |-- ii. Style Transfer
|       -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
|   |-- a. Hadoop
|   |   |-- i. HDFS
|   |   -- ii. MapReduce
|   |
|   |-- b. Spark
|   |   |-- i. RDDs
|   |   |-- ii. DataFrames
|   |   -- iii. MLlib
|   |
|   -- c. NoSQL Databases
|       |-- i. MongoDB
|       |-- ii. Cassandra
|       |-- iii. HBase
|       -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
|   |-- a. Dashboarding Tools
|   |   |-- i. Tableau
|   |   |-- ii. Power BI
|   |   |-- iii. Dash (Python)
|   |   -- iv. Shiny (R)
|   |
|   |-- b. Storytelling with Data
|   -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
|   |-- a. Industry-specific Knowledge
|   |-- b. Problem-solving
|   |-- c. Communication Skills
|   |-- d. Time Management
|   -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
    |-- a. Online Courses
    |-- b. Books and Research Papers
    |-- c. Blogs and Podcasts
    |-- d. Conferences and Workshops
    `-- e. Networking and Community Engagement

The Foundation of Data Science

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These free, Microsoft-backed courses are a game-changer!

With these resources, you’ll gain the skills and confidence needed to shine in the data analytics world—all without spending a penny.

𝐋𝐢𝐧𝐤 👇:- 

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𝗟𝗲𝗮𝗿𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜 𝗳𝗼𝗿 𝗙𝗥𝗘𝗘 & 𝗘𝗹𝗲𝘃𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘀𝗵𝗯𝗼𝗮𝗿𝗱 𝗚𝗮𝗺𝗲!😍

Want to turn raw data into stunning visual stories?📊

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Enjoy Learning ✅️

The Data Science skill no one talks about...

Every aspiring data scientist I talk to thinks their job starts when someone else gives them:
    1. a dataset, and
    2. a clearly defined metric to optimize for, e.g. accuracy

But it doesn’t.

It starts with a business problem you need to understand, frame, and solve. This is the key data science skill that separates senior from junior professionals.

Let’s go through an example.

Example

Imagine you are a data scientist at Uber. And your product lead tells you:

    👩‍💼: “We want to decrease user churn by 5% this quarter”

We say that a user churns when she decides to stop using Uber.

But why?

There are different reasons why a user would stop using Uber. For example:

   1.  “Lyft is offering better prices for that geo” (pricing problem)
   2. “Car waiting times are too long” (supply problem)
   3. “The Android version of the app is very slow” (client-app performance problem)

You build this list ↑ by asking the right questions to the rest of the team. You need to understand the user’s experience using the app, from HER point of view.

Typically there is no single reason behind churn, but a combination of a few of these. The question is: which one should you focus on?

This is when you pull out your great data science skills and EXPLORE THE DATA 🔎.

You explore the data to understand how plausible each of the above explanations is. The output from this analysis is a single hypothesis you should consider further. Depending on the hypothesis, you will solve the data science problem differently.

For example…

Scenario 1: “Lyft Is Offering Better Prices” (Pricing Problem)

One solution would be to detect/predict the segment of users who are likely to churn (possibly using an ML Model) and send personalized discounts via push notifications. To test your solution works, you will need to run an A/B test, so you will split a percentage of Uber users into 2 groups:

    The A group. No user in this group will receive any discount.

    The B group. Users from this group that the model thinks are likely to churn, will receive a price discount in their next trip.

You could add more groups (e.g. C, D, E…) to test different pricing points.

In a nutshell

    1. Translating business problems into data science problems is the key data science skill that separates a senior from a junior data scientist.
2. Ask the right questions, list possible solutions, and explore the data to narrow down the list to one.
3. Solve this one data science problem