Programmers, if your resume isn't getting you job interviews, here's what you're doing wrong:

1. Limiting your scope

Don’t say “React Developer” ❌
Say “Frontend Developer” ✅

Your projects in React will prove understand frontend well and are capable of learning more frameworks.

2. Being very general in your experience/internships section.

Don’t say “Created apis for frontend” ❌

Say “Created express nodejs apis to fetch user data from Cosmos DB” ✅

It helps with keyword matching & also improves credibility of your statement

3. Focusing on certifications instead of projects

“Completed ‘Introduction to JAVA’ course on Udemy” has low/no value. ❌

“Built a JAVA springboot application…” has high value ✅

4. Not being relavent enough

Mentioning your Machine Learning projects for a frontend engineer position isn’t relavent.

Similarly expanding your “Digital Marketer” experience into 6 big points isn’t gonna help for a Software Engineer position.

Keep irrelevant experiences short

8 trading and backtesting libraries in Python that will help you find your edge:

QuantSoftware Toolkit

Python-based open source software framework designed to support portfolio construction and

quantitative

Quantitative finance, and backtesting

analyzer

Python framework for real-time financial and backtesting trading

pybacktest

Vectorized backtesting framework in Python / pandas, designed to make your backtesting

*** Positional option selling strategy in Bank nifty ***
1. Entry 3.20 PM
2. Exit Next day 9.30 AM
Over night risk is defined and reward is too Good.
Capital Below 1L . Return 0.5 to 1% return in over night .
if your view went wrong also you can make good money by doing this.

Now Bank nifty closing spot price is 39300.
Your view is side ways to mild bullish for next day. So you can enter put butterfly at 3.20 PM
Buy - 39500 pe ITM
sell - 39100 pe just OTM
buy - 38800 pe OTM
R:R is 3 times

Case 1: As per your view market opened bullish you will be in profit.
Case 2: As per your view market opened side ways you will be in profit
Case 3: Market opened against your view till 1% gap down you will be in profit and loss also defined .

If market opened against to your direction by move 38800 pe to 38900 pe you can minimize loss on down side .

Your view is side ways to mild bearish for next day. So you can enter call butterfly at 3.20 PM
Buy - 39100 ce ITM
sell - 39300 ce ATM
buy - 39500 ce OTM
R:R is 4 times

A past coaching client of mine interviewed for an entry-level Data Science job in Los Angeles that paid $110k per year.

Here's the 19 interview questions they were asked:

(how many of these could you answer?)

𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 🔢

1. Explain the difference between population variance vs sample variance

2. Describe a p-value to a 13-year-old (8th grader)

3. What Does an R-Squared Value of 0.9 Mean?

𝐒𝐭𝐚𝐭𝐢𝐬𝐭𝐢𝐜𝐬 (Continued) 🔢

4. What are some metrics to evaluate how predictive your logistic regression model is?

5. Can you explain precision vs. recall? How does that relate to an ROC curve?

𝐒𝐐𝐋 ⚙️

6. What is a database view?

7. What's the difference between a primary key & a foreign key?

8. Have you had to troubleshoot a slow query before? How did you do it?

9. When should you use a Sub-Query vs. CTE?

𝐒𝐐𝐋 (Continued) ⚙️

10. Can you write a SQL query to find the 3-day rolling average?

(very similar to this Twitter SQL interview question on

Machine Learning projects to boost your portfolio 🚀

🧵 👇

1️⃣ mRNA Degradation using Deep Learning:

Check this out 👇

2️⃣ The Self Driving car project:

Check this out 👇

3️⃣ Stock Price Forecasting using Deep Learning:

Check this out 👇

4️⃣ Build a Conversational AI Bot:

Check this out 👇

9 trading strategies everyone should know (with Python code):

Bollinger Bands Pattern Recognition

The mid band is the moving average on the price series.

The upper and lower bands are two moving standard deviations away from the mid band.

MACD oscillator

MACD refers to Moving Average Convergence/Divergence.

MACD is a momentum trading strategy.

It assumes momentum has more impact on short-term moving average than long-term moving average.

Pairs Trading

Pairs trading is a basic form of statistical arbitrage.

It assumes that two cointegrated stocks do not drift too far away from each other.

When they do, it generates the trading signal.

Heikin-Ashi Candlestick

Heikin-Ashi refers to 'Average Bar' in Japanese.

It's an alternative style of candlestick chart.

The rules of Heiki-Ashi are designed to filter out the noise for momentum trading.

Myth:

You need a computer science degree to get started with Python.

Reality:

You need YouTube.

Get started with these 7 channels:

Anaconda Inc.

Anaconda is the world’s most popular Python data science platform. Videos directly from the folks that built

CodingEntreprenuers

Coding for Entrepreneurs is a programming series for non-technical

Enthought

Enthought has built AI solutions with science and engineering at the core. In addition to the company-developed content, it has tutorials specifically focused on the advancement of scientific

Michael Kennedy (Talk Python)

Videos, demos, and lectures about Python and web

5️⃣ Mathematics for Machine Learning resources you must check! ✅

🧵 👇

1️⃣ StatQuest:

BAM!!

Joshua Stammer's fun take on teaching Stats & Mathematics for ML to masses is an absolute gold mine 🥇

Check this out 👇

2️⃣ Statistics, Khan Academy:

Taught by Sal Khan himself ✨

It's a top-top place to learn Prob/Stats 👍

💫 I still refer it ✅

Check this out 👇

3️⃣ MIT OCW: Mathematics for ML

The course offers a comprehensive overview of mathematics for big data and ML.

It comes with lecture notes and exercises from MIT.

Check out the playlist 👇

4️⃣ Linear Algebra by Gilbert Strang:

Taught by the pioneer himself, Prof. Gilbert Strang. ✨

There isn't a better place to learn Algebra in the entire world. 👊

Check this out 👇

Just built the first ML web app using streamlit to classify 6 categories of natural images🚀

From notebook to deployment

building a data science web app is real fun⭐🔥

will be sharing public link by tommorrow

I've been using pandas since 2011.

It's the most important tool for analysts working in Python today.

If you're using Python, you need pandas.

Here's the 7 tutorials I wish I had when I started:

10 minutes to pandas

This is a short introduction to pandas, geared mainly for new

pandas tutorial on W3 Schools

A 14 section getting started guide for pandas. From an introduction to the library to more advanced features like correlation and

A Complete Introduction for Beginners

Learn some of the most important pandas features for exploring, cleaning, transforming, visualizing, and learning from

Hands-On Pandas

Solve short hands-on challenges to perfect your data manipulation

If you are prone to overthinking, read this:

For years I struggled with overthinking.

It was downright debilitating.

Fortunately, I found some strategies that help.

Here are 7 concepts to limit overthinking (and live your best life):

1. Morning Pages (Julia Child Method)

Empty your mind’s confusing thoughts on paper.

You’ll fill your day with clearer thinking.

The secret? Long-form journaling.

Here’s how:

1. Block out 45 mins
2. Grab a pen and journal
3. Write stream-of-consciousness (unfiltered)

2. 5 Second Rule

“The moment you have an instinct to act on a goal you must 5-4-3-2-1 and physically move or your brain will stop you.” —Mel Robbins.

The 5s Rule improves your ‘locus of control.’

It helps you flip the switch from uncontrolled anxiety to excitement…

According to neuroscientists, 95% of our decisions are based on feelings.

The problem with that:

You must first change your behavior to change how you feel.

In the words of Aristotle, “Do good, feel good.”

Like jumping in a pool: Count down from 5, and take action.

Best Python libraries for Machine Learning that are open source 💯

A summary about each with their GitHub link 😉

Thread 🧵👇

1. Pandas

It aims to be the fundamental high-level building block for practical, real-world data analysis in Python.

🔗

2. OpenCV

It has more than 2500 highly optimised algorithms for machine learning and computer vision that can do just about anything with images.

🔗

3. Matplotlib

Matplotlib is a comprehensive library for creating static, animated, and interactive visualizations in Python.

🔗

4. Tensorflow

It's an end-to-end Machine Learning and Deep Learning library to solve real-world

Machine Learning Projects with source code
in Python😲

A thread 🧵👇

1. HealthCure — medical project — 7 disease

2. Object Detection using

3. Social Distancing using YOLOv3

4. Wine Quality

My master's degree completely failed to teach me how to test trading strategies.

So I spent 40 hours looking for Python backtesting libraries.

Then I started using the best ones.

But unlike my quant finance degree, these won't cost you $90,000.

Here they are for free.

AutoTrader

AutoTrader is Python-based platform intended to help in the development, optimisation and deployment of automated trading

Zipline

From Quantopian (acquired by Robinhood) the first to democratize quant trading, comes Zipline. It's a robust, fully-featured backtesting library which features slippage models, robust data handing and rich

backtrader

backtrader features live data and trading, filters and multiple data feeds at

backtesting .py

backtesting .py features a simple well-documented API, fast execution and a library of built-in base