How to get freelancing clients for data science projects
👇👇
https://t.iss.one/freelancing_upwork/17

Here are a few Data Science/AI/ML project ideas that could help you stand out:

Quantitative Analysis of Financial Data: Create a project where you analyze historical financial data using statistical methods and time series analysis to identify patterns, correlations, and trends in the data.

Development of Trading Strategies: Design and backtest quantitative trading strategies using historical market data. Showcase your ability to develop, test, and optimize algorithmic trading models.
Risk Management Simulation: Build a simulation model to assess and manage financial risk. This could involve implementing Value at Risk (VaR) models or stress testing methodologies.

Machine Learning for Finance: Explore the application of machine learning algorithms to financial markets. Develop a project that uses machine learning for stock price prediction, sentiment analysis of news articles, or credit risk assessment.

Financial Modeling and Valuation: Create detailed financial models for companies or investment opportunities. This could include building discounted cash flow (DCF) models, comparable company analysis, and merger and acquisition (M&A) valuation.

Portfolio Optimization: Develop a project that focuses on portfolio optimization techniques, such as modern portfolio theory, mean-variance optimization, or factor modeling.

By working on these projects, you can demonstrate your skills in quantitative analysis, financial modeling, and programming, which are highly valued in the field of quantitative finance.

Additionally, consider sharing your projects on platforms like GitHub or creating a personal website to showcase your work to potential employers.

5 signs of an inexperienced self-taught data scientist

- Not evaluating their model metrics
- Not considering the presentation and readability of code
- Focusing on multiple tools and tasks at once
- Inability to transfer business problems into data algorithms
- No habit of systematic learning and continuous improvement

You're not a Data Scientist until you have...


☑ Built a dashboard that no one uses
☑ Googled the same SQL window function for the 100th time
☑ Convinced yourself that the weird outlier is definitely not a bug
☑ Built a complex analysis in Python when you could have done it in SQL
☑ Broken a data pipeline because you "forgot" to test before pushing to prod
☑ Tell your mom for the 8374th time that you're not a real scientist, and she should stop telling her friends that

<insert more here>

What else did I miss?

Platforms to learn Data Science
👇👇
https://www.linkedin.com/posts/sql-analysts_datascience-machinelearning-ai-activity-7208370270485594112-8ODh

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

Best Data Science & Machine Learning Resources: https://topmate.io/coding/914624

All the best 👍👍

The 80/20 Principle:

- Health: 80% eating, 20% excercising
- Wealth: 80% habits, 20% math
- Talking: 80% listening, 20% speaking
- Learning: 80% understanding, 20% reading
- Achieving: 80% doing, 20% dreaming
- Happiness: 80% purpose, 20% fun
- Relationships: 80% giving, 20% receiving
- Improving: 80% persistence, 20% ideas

Priories the 80% and the rest will fall into place

What roles make it easier to get into Data Science?

Most of Data Scientists usually transitioned in from other roles

The most common ones, are - Data Analyst, Business Intelligence Engineer and Data Engineer.

For a fresher with only a bachelors degree, I would advise the Data Analyst role. Based on the team and work, you may in essence be able to work as a Data Scientist.

Hi guys,

This post is for all those who are confused with which path to go. Whether ai will take over what they're learning. See one thing is for sure, life is random, now ai is trending and in future it might be something else. So better be prepared with whatever comes up and never stop learning. Recently, I got a lesson from my friend. He follow a very good habit of investing money in stocks or crypto. One of my friend has earned around 90lakh+ by investing on a single stock. Even though, previously he encured some losses before earning this profit.

But investing is really a very good skill to have. I never did it before but now, I am also planning to learn investment skill moving further.

Investing allows you to put your money into businesses you believe in, potentially growing your wealth over time. It can also provide financial security and help you achieve your long-term financial goals, such as buying a home, funding education, or planning for retirement.

I will share my learnings with you guys here 👇👇
https://t.iss.one/stockmarketinginsights

Learning some additional skills gives you advantage of fighting with uncertainties in life. So, don't think too much before learning any new skill. Even though it's not required for now, but who knows if that's super useful in future. Keep learning & never give up.

What are your thoughts guys, let me know in comments 👇👇

One of the very important and underrated skills while learning data science, machine learning, or any other new skill is patience.

Everything takes time, but patience helps you stay calm and focused. Learn from your mistakes, keep practicing, and steadily improve.

These early struggles will slowly turn into success 😄💪

What is your preferred programming language for data manipulation?

1. Python
2. R
3. Julia
4. MATLAB
5. SAS

Feel free to mention any other language you prefer in the comments! 👇👇

How do we evaluate classification models?

Depending on the classification problem, we can use the following evaluation metrics:

Accuracy
Precision
Recall
F1 Score
Logistic loss (also known as Cross-entropy loss)
Jaccard similarity coefficient score

Which machine learning framework do you find most effective?

1. TensorFlow
2. PyTorch
3. Scikit-learn
4. Keras
5. XGBoost

If you have a different favorite, share it in the comments below! 👇👇

Where to get data for your next machine learning project?

An overview of 5 amazing resources to accelerate your next project with data!

📌 Google Datasets
Easy to search Datasets on Google Dataset Search engine as it is to search for anything on Google Search! You just enter the topic on which you need to find a Dataset.

📌 Kaggle Dataset
Explore, analyze, and share quality data.

📌 Open Data on AWS
This registry exists to help people discover and share datasets that are available via AWS resources

📌 Awesome Public Datasets
A topic-centric list of HQ open datasets.

📌 Azure public data sets
Public data sets for testing and prototyping.