Python Cheatsheet ๐
1๏ธโฃ Variables & Data Types
x = 10 (Integer)
y = 3.14 (Float)
name = "Python" (String)
is_valid = True (Boolean)
items = [1, 2, 3] (List)
data = (1, 2, 3) (Tuple)
person = {"name": "Alice", "age": 25} (Dictionary)
2๏ธโฃ Operators
Arithmetic: +, -, *, /, //, %, **
Comparison: ==, !=, >, <, >=, <=
Logical: and, or, not
Membership: in, not in
3๏ธโฃ Control Flow
If-Else:
if age > 18:
print("Adult")
elif age == 18:
print("Just turned 18")
else:
print("Minor")
Loops:
for i in range(5):
print(i)
while x < 10:
x += 1
4๏ธโฃ Functions
Defining & Calling:
def greet(name):
return f"Hello, {name}"
print(greet("Alice"))
Lambda Functions: add = lambda x, y: x + y
5๏ธโฃ Lists & Dictionary Operations
Append: items.append(4)
Remove: items.remove(2)
List Comprehension: [x**2 for x in range(5)]
Dictionary Access: person["name"]
6๏ธโฃ File Handling
Read File:
with open("file.txt", "r") as f:
content = f.read()
Write File:
with open("file.txt", "w") as f:
f.write("Hello, World!")
7๏ธโฃ Exception Handling
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
finally:
print("Done")
8๏ธโฃ Modules & Packages
Importing:
import math
print(math.sqrt(25))
Creating a Module (mymodule.py):
def add(x, y):
return x + y
Usage: from mymodule import add
9๏ธโฃ Object-Oriented Programming (OOP)
Defining a Class:
class Person:
def init(self, name, age):
self.name = name
self.age = age
def greet(self):
return f"Hello, my name is {self.name}"
Creating an Object: p = Person("Alice", 25)
๐ Useful Libraries
NumPy: import numpy as np
Pandas: import pandas as pd
Matplotlib: import matplotlib.pyplot as plt
Requests: import requests
Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
ENJOY LEARNING ๐๐
1๏ธโฃ Variables & Data Types
x = 10 (Integer)
y = 3.14 (Float)
name = "Python" (String)
is_valid = True (Boolean)
items = [1, 2, 3] (List)
data = (1, 2, 3) (Tuple)
person = {"name": "Alice", "age": 25} (Dictionary)
2๏ธโฃ Operators
Arithmetic: +, -, *, /, //, %, **
Comparison: ==, !=, >, <, >=, <=
Logical: and, or, not
Membership: in, not in
3๏ธโฃ Control Flow
If-Else:
if age > 18:
print("Adult")
elif age == 18:
print("Just turned 18")
else:
print("Minor")
Loops:
for i in range(5):
print(i)
while x < 10:
x += 1
4๏ธโฃ Functions
Defining & Calling:
def greet(name):
return f"Hello, {name}"
print(greet("Alice"))
Lambda Functions: add = lambda x, y: x + y
5๏ธโฃ Lists & Dictionary Operations
Append: items.append(4)
Remove: items.remove(2)
List Comprehension: [x**2 for x in range(5)]
Dictionary Access: person["name"]
6๏ธโฃ File Handling
Read File:
with open("file.txt", "r") as f:
content = f.read()
Write File:
with open("file.txt", "w") as f:
f.write("Hello, World!")
7๏ธโฃ Exception Handling
try:
result = 10 / 0
except ZeroDivisionError:
print("Cannot divide by zero!")
finally:
print("Done")
8๏ธโฃ Modules & Packages
Importing:
import math
print(math.sqrt(25))
Creating a Module (mymodule.py):
def add(x, y):
return x + y
Usage: from mymodule import add
9๏ธโฃ Object-Oriented Programming (OOP)
Defining a Class:
class Person:
def init(self, name, age):
self.name = name
self.age = age
def greet(self):
return f"Hello, my name is {self.name}"
Creating an Object: p = Person("Alice", 25)
๐ Useful Libraries
NumPy: import numpy as np
Pandas: import pandas as pd
Matplotlib: import matplotlib.pyplot as plt
Requests: import requests
Python Resources: https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
ENJOY LEARNING ๐๐
โค5๐1
Here's a concise cheat sheet to help you get started with Python for Data Analytics. This guide covers essential libraries and functions that you'll frequently use.
1. Python Basics
- Variables:
- Data Types:
- Integers:
- Control Structures:
-
- Loops:
- While loop:
2. Importing Libraries
- NumPy:
- Pandas:
- Matplotlib:
- Seaborn:
3. NumPy for Numerical Data
- Creating Arrays:
- Array Operations:
- Reshaping Arrays:
- Indexing and Slicing:
4. Pandas for Data Manipulation
- Creating DataFrames:
- Reading Data:
- Basic Operations:
- Selecting Columns:
- Filtering Data:
- Handling Missing Data:
- GroupBy:
5. Data Visualization
- Matplotlib:
- Seaborn:
6. Common Data Operations
- Merging DataFrames:
- Pivot Table:
- Applying Functions:
7. Basic Statistics
- Descriptive Stats:
- Correlation:
This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features.
I have curated the best resources to learn Python ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
1. Python Basics
- Variables:
x = 10 y = "Hello"
- Data Types:
- Integers:
x = 10
- Floats: y = 3.14
- Strings: name = "Alice"
- Lists: my_list = [1, 2, 3]
- Dictionaries: my_dict = {"key": "value"}
- Tuples: my_tuple = (1, 2, 3)
- Control Structures:
-
if, elif, else statements- Loops:
for i in range(5):
print(i)
- While loop:
while x < 5:
print(x)
x += 1
2. Importing Libraries
- NumPy:
import numpy as np
- Pandas:
import pandas as pd
- Matplotlib:
import matplotlib.pyplot as plt
- Seaborn:
import seaborn as sns
3. NumPy for Numerical Data
- Creating Arrays:
arr = np.array([1, 2, 3, 4])
- Array Operations:
arr.sum()
arr.mean()
- Reshaping Arrays:
arr.reshape((2, 2))
- Indexing and Slicing:
arr[0:2] # First two elements
4. Pandas for Data Manipulation
- Creating DataFrames:
df = pd.DataFrame({
'col1': [1, 2, 3],
'col2': ['A', 'B', 'C']
})
- Reading Data:
df = pd.read_csv('file.csv')
- Basic Operations:
df.head() # First 5 rows
df.describe() # Summary statistics
df.info() # DataFrame info
- Selecting Columns:
df['col1']
df[['col1', 'col2']]
- Filtering Data:
df[df['col1'] > 2]
- Handling Missing Data:
df.dropna() # Drop missing values
df.fillna(0) # Replace missing values
- GroupBy:
df.groupby('col2').mean()
5. Data Visualization
- Matplotlib:
plt.plot(df['col1'], df['col2'])
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Title')
plt.show()
- Seaborn:
sns.histplot(df['col1'])
sns.boxplot(x='col1', y='col2', data=df)
6. Common Data Operations
- Merging DataFrames:
pd.merge(df1, df2, on='key')
- Pivot Table:
df.pivot_table(index='col1', columns='col2', values='col3')
- Applying Functions:
df['col1'].apply(lambda x: x*2)
7. Basic Statistics
- Descriptive Stats:
df['col1'].mean()
df['col1'].median()
df['col1'].std()
- Correlation:
df.corr()
This cheat sheet should give you a solid foundation in Python for data analytics. As you get more comfortable, you can delve deeper into each library's documentation for more advanced features.
I have curated the best resources to learn Python ๐๐
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
Hope you'll like it
Like this post if you need more resources like this ๐โค๏ธ
โค12
If I Were to Start My Data Science Career from Scratch, Here's What I Would Do ๐
1๏ธโฃ Master Advanced SQL
Foundations: Learn database structures, tables, and relationships.
Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.
Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.
JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.
Advanced Concepts: CTEs, window functions, and query optimization.
Metric Development: Build and report metrics effectively.
2๏ธโฃ Study Statistics & A/B Testing
Descriptive Statistics: Know your mean, median, mode, and standard deviation.
Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.
Probability: Understand basic probability and Bayes' theorem.
Intro to ML: Start with linear regression, decision trees, and K-means clustering.
Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.
A/B Testing: Design experimentsโhypothesis formation, sample size calculation, and sample biases.
3๏ธโฃ Learn Python for Data
Data Manipulation: Use pandas for data cleaning and manipulation.
Data Visualization: Explore matplotlib and seaborn for creating visualizations.
Hypothesis Testing: Dive into scipy for statistical testing.
Basic Modeling: Practice building models with scikit-learn.
4๏ธโฃ Develop Product Sense
Product Management Basics: Manage projects and understand the product life cycle.
Data-Driven Strategy: Leverage data to inform decisions and measure success.
Metrics in Business: Define and evaluate metrics that matter to the business.
5๏ธโฃ Hone Soft Skills
Communication: Clearly explain data findings to technical and non-technical audiences.
Collaboration: Work effectively in teams.
Time Management: Prioritize and manage projects efficiently.
Self-Reflection: Regularly assess and improve your skills.
6๏ธโฃ Bonus: Basic Data Engineering
Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.
ETL: Set up extraction jobs, manage dependencies, clean and validate data.
Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
1๏ธโฃ Master Advanced SQL
Foundations: Learn database structures, tables, and relationships.
Basic SQL Commands: SELECT, FROM, WHERE, ORDER BY.
Aggregations: Get hands-on with SUM, COUNT, AVG, MIN, MAX, GROUP BY, and HAVING.
JOINs: Understand LEFT, RIGHT, INNER, OUTER, and CARTESIAN joins.
Advanced Concepts: CTEs, window functions, and query optimization.
Metric Development: Build and report metrics effectively.
2๏ธโฃ Study Statistics & A/B Testing
Descriptive Statistics: Know your mean, median, mode, and standard deviation.
Distributions: Familiarize yourself with normal, Bernoulli, binomial, exponential, and uniform distributions.
Probability: Understand basic probability and Bayes' theorem.
Intro to ML: Start with linear regression, decision trees, and K-means clustering.
Experimentation Basics: T-tests, Z-tests, Type 1 & Type 2 errors.
A/B Testing: Design experimentsโhypothesis formation, sample size calculation, and sample biases.
3๏ธโฃ Learn Python for Data
Data Manipulation: Use pandas for data cleaning and manipulation.
Data Visualization: Explore matplotlib and seaborn for creating visualizations.
Hypothesis Testing: Dive into scipy for statistical testing.
Basic Modeling: Practice building models with scikit-learn.
4๏ธโฃ Develop Product Sense
Product Management Basics: Manage projects and understand the product life cycle.
Data-Driven Strategy: Leverage data to inform decisions and measure success.
Metrics in Business: Define and evaluate metrics that matter to the business.
5๏ธโฃ Hone Soft Skills
Communication: Clearly explain data findings to technical and non-technical audiences.
Collaboration: Work effectively in teams.
Time Management: Prioritize and manage projects efficiently.
Self-Reflection: Regularly assess and improve your skills.
6๏ธโฃ Bonus: Basic Data Engineering
Data Modeling: Understand dimensional modeling and trade-offs in normalization vs. denormalization.
ETL: Set up extraction jobs, manage dependencies, clean and validate data.
Pipeline Testing: Conduct unit testing and ensure data quality throughout the pipeline.
I have curated the best interview resources to crack Data Science Interviews
๐๐
https://whatsapp.com/channel/0029Va8v3eo1NCrQfGMseL2D
Like if you need similar content ๐๐
โค3
Important Topics You Should Know to Learn Python ๐
Lists, Strings, Tuples, Dictionaries, Sets โ Learn the core data structures in Python.
Boolean, Arithmetic, and Comparison Operators โ Understand how Python evaluates conditions.
Operations on Data Structures โ Append, delete, insert, reverse, sort, and manipulate collections efficiently.
Reading and Extracting Data โ Learn how to access, modify, and extract values from lists and dictionaries.
Conditions and Loops โ Master if, elif, else, for, while, break, and continue statements.
Range and Enumerate โ Efficiently loop through sequences with indexing.
Functions โ Create functions with and without parameters, and understand *args and **kwargs.
Classes & Object-Oriented Programming โ Work with init methods, global/local variables, and concepts like inheritance and encapsulation.
File Handling โ Read, write, and manipulate files in Python.
Free Resources to learn Python๐๐
๐ Free Python course by Google
https://developers.google.com/edu/python
๐ Freecodecamp Python course
https://www.freecodecamp.org/learn/data-analysis-with-python/#
๐ Udacity Intro to Python course
https://bit.ly/3FOOQHh
๐Python Cheatsheet
https://t.iss.one/pythondevelopersindia/262?single
๐ Practice Python
https://www.pythonchallenge.com/
๐ Kaggle
https://kaggle.com/learn/intro-to-programming
https://kaggle.com/learn/python
๐ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น๐ ๐ถ๐ป ๐ฃ๐๐๐ต๐ผ๐ป
https://netacad.com/courses/programming/pcap-programming-essentials-python
๐ Python Essentials
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
https://t.iss.one/dsabooks
๐ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐ณ๐ถ๐ฐ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
https://freecodecamp.org/learn/scientific-computing-with-python/
๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
https://freecodecamp.org/learn/data-analysis-with-python/
๐ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
https://freecodecamp.org/learn/machine-learning-with-python/
ENJOY LEARNING ๐๐
Lists, Strings, Tuples, Dictionaries, Sets โ Learn the core data structures in Python.
Boolean, Arithmetic, and Comparison Operators โ Understand how Python evaluates conditions.
Operations on Data Structures โ Append, delete, insert, reverse, sort, and manipulate collections efficiently.
Reading and Extracting Data โ Learn how to access, modify, and extract values from lists and dictionaries.
Conditions and Loops โ Master if, elif, else, for, while, break, and continue statements.
Range and Enumerate โ Efficiently loop through sequences with indexing.
Functions โ Create functions with and without parameters, and understand *args and **kwargs.
Classes & Object-Oriented Programming โ Work with init methods, global/local variables, and concepts like inheritance and encapsulation.
File Handling โ Read, write, and manipulate files in Python.
Free Resources to learn Python๐๐
๐ Free Python course by Google
https://developers.google.com/edu/python
๐ Freecodecamp Python course
https://www.freecodecamp.org/learn/data-analysis-with-python/#
๐ Udacity Intro to Python course
https://bit.ly/3FOOQHh
๐Python Cheatsheet
https://t.iss.one/pythondevelopersindia/262?single
๐ Practice Python
https://www.pythonchallenge.com/
๐ Kaggle
https://kaggle.com/learn/intro-to-programming
https://kaggle.com/learn/python
๐ ๐ฃ๐ฟ๐ผ๐ด๐ฟ๐ฎ๐บ๐บ๐ถ๐ป๐ด ๐๐๐๐ฒ๐ป๐๐ถ๐ฎ๐น๐ ๐ถ๐ป ๐ฃ๐๐๐ต๐ผ๐ป
https://netacad.com/courses/programming/pcap-programming-essentials-python
๐ Python Essentials
https://whatsapp.com/channel/0029VaiM08SDuMRaGKd9Wv0L
https://t.iss.one/dsabooks
๐ ๐ฆ๐ฐ๐ถ๐ฒ๐ป๐๐ถ๐ณ๐ถ๐ฐ ๐๐ผ๐บ๐ฝ๐๐๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
https://freecodecamp.org/learn/scientific-computing-with-python/
๐ ๐๐ฎ๐๐ฎ ๐๐ป๐ฎ๐น๐๐๐ถ๐ ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
https://freecodecamp.org/learn/data-analysis-with-python/
๐ ๐ ๐ฎ๐ฐ๐ต๐ถ๐ป๐ฒ ๐๐ฒ๐ฎ๐ฟ๐ป๐ถ๐ป๐ด ๐๐ถ๐๐ต ๐ฃ๐๐๐ต๐ผ๐ป
https://freecodecamp.org/learn/machine-learning-with-python/
ENJOY LEARNING ๐๐
โค4๐1
๐ Complete Roadmap to Become a Data Scientist in 5 Months
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
๐ Week 1-2: Fundamentals
โ Day 1-3: Introduction to Data Science, its applications, and roles.
โ Day 4-7: Brush up on Python programming ๐.
โ Day 8-10: Learn basic statistics ๐ and probability ๐ฒ.
๐ Week 3-4: Data Manipulation & Visualization
๐ Day 11-15: Master Pandas for data manipulation.
๐ Day 16-20: Learn Matplotlib & Seaborn for data visualization.
๐ค Week 5-6: Machine Learning Foundations
๐ฌ Day 21-25: Introduction to scikit-learn.
๐ Day 26-30: Learn Linear & Logistic Regression.
๐ Week 7-8: Advanced Machine Learning
๐ณ Day 31-35: Explore Decision Trees & Random Forests.
๐ Day 36-40: Learn Clustering (K-Means, DBSCAN) & Dimensionality Reduction.
๐ง Week 9-10: Deep Learning
๐ค Day 41-45: Basics of Neural Networks with TensorFlow/Keras.
๐ธ Day 46-50: Learn CNNs & RNNs for image & text data.
๐ Week 11-12: Data Engineering
๐ Day 51-55: Learn SQL & Databases.
๐งน Day 56-60: Data Preprocessing & Cleaning.
๐ Week 13-14: Model Evaluation & Optimization
๐ Day 61-65: Learn Cross-validation & Hyperparameter Tuning.
๐ Day 66-70: Understand Evaluation Metrics (Accuracy, Precision, Recall, F1-score).
๐ Week 15-16: Big Data & Tools
๐ Day 71-75: Introduction to Big Data Technologies (Hadoop, Spark).
โ๏ธ Day 76-80: Learn Cloud Computing (AWS, GCP, Azure).
๐ Week 17-18: Deployment & Production
๐ Day 81-85: Deploy models using Flask or FastAPI.
๐ฆ Day 86-90: Learn Docker & Cloud Deployment (AWS, Heroku).
๐ฏ Week 19-20: Specialization
๐ Day 91-95: Choose NLP or Computer Vision, based on your interest.
๐ Week 21-22: Projects & Portfolio
๐ Day 96-100: Work on Personal Data Science Projects.
๐ฌ Week 23-24: Soft Skills & Networking
๐ค Day 101-105: Improve Communication & Presentation Skills.
๐ Day 106-110: Attend Online Meetups & Forums.
๐ฏ Week 25-26: Interview Preparation
๐ป Day 111-115: Practice Coding Interviews (LeetCode, HackerRank).
๐ Day 116-120: Review your projects & prepare for discussions.
๐จโ๐ป Week 27-28: Apply for Jobs
๐ฉ Day 121-125: Start applying for Entry-Level Data Scientist positions.
๐ค Week 29-30: Interviews
๐ Day 126-130: Attend Interviews & Practice Whiteboard Problems.
๐ Week 31-32: Continuous Learning
๐ฐ Day 131-135: Stay updated with the Latest Data Science Trends.
๐ Week 33-34: Accepting Offers
๐ Day 136-140: Evaluate job offers & Negotiate Your Salary.
๐ข Week 35-36: Settling In
๐ฏ Day 141-150: Start your New Data Science Job, adapt & keep learning!
๐ Enjoy Learning & Build Your Dream Career in Data Science! ๐๐ฅ
โค5
Essential Python Libraries for Data Science
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING ๐๐
- Numpy: Fundamental for numerical operations, handling arrays, and mathematical functions.
- SciPy: Complements Numpy with additional functionalities for scientific computing, including optimization and signal processing.
- Pandas: Essential for data manipulation and analysis, offering powerful data structures like DataFrames.
- Matplotlib: A versatile plotting library for creating static, interactive, and animated visualizations.
- Keras: A high-level neural networks API, facilitating rapid prototyping and experimentation in deep learning.
- TensorFlow: An open-source machine learning framework widely used for building and training deep learning models.
- Scikit-learn: Provides simple and efficient tools for data mining, machine learning, and statistical modeling.
- Seaborn: Built on Matplotlib, Seaborn enhances data visualization with a high-level interface for drawing attractive and informative statistical graphics.
- Statsmodels: Focuses on estimating and testing statistical models, providing tools for exploring data, estimating models, and statistical testing.
- NLTK (Natural Language Toolkit): A library for working with human language data, supporting tasks like classification, tokenization, stemming, tagging, parsing, and more.
These libraries collectively empower data scientists to handle various tasks, from data preprocessing to advanced machine learning implementations.
ENJOY LEARNING ๐๐
โค4๐1
AI Engineers can be quite successful in this role without ever training anything.
This is how:
1/ Leveraging pre-trained LLMs: Select and tune existing LLMs for specific tasks. Don't start from scratch
2/ Prompt engineering: Craft effective prompts to optimize LLM performance without model modifications
3/ Implement Modern AI Solution Architectures: Design systems like RAG to enhance LLMs with external knowledge
Developers: The barrier to entry is lower than ever.
Focus on the solution's VALUE and connect AI components like you were assembling Lego! (Credits: Unknown)
This is how:
1/ Leveraging pre-trained LLMs: Select and tune existing LLMs for specific tasks. Don't start from scratch
2/ Prompt engineering: Craft effective prompts to optimize LLM performance without model modifications
3/ Implement Modern AI Solution Architectures: Design systems like RAG to enhance LLMs with external knowledge
Developers: The barrier to entry is lower than ever.
Focus on the solution's VALUE and connect AI components like you were assembling Lego! (Credits: Unknown)
โค4
Do these 4 things to 10x your responses while asking for referrals:
1. Be personal. (never use AI)
I get a ton of messages that are either written by AI or obviously copy and pasted to 100 people.
Be personal by mentioning something you have in common with the person youโre messaging or what you got out of one of their posts.
2. Have a specific job that you want to apply for and send the link.
โCan you look and see if there are any openings?โ is incredibly rude and inconsiderate of the personโs time.
If you want them to help you with a referral, do the work for them by sending them the link, why youโre a good fit, and other needed info.
3. Reach out to people who are active on LinkedIn, but not content creators.
Everytime thereโs an opening at my company, I get 50 messages asking for a referral. As much as I want to, I canโt refer everyone.
Therefore, look for those to connect with at a company youโre interested in that post occasionally on LinkedIn, but are not content creators.
These people will be active enough to see your message, but not have 3 dozen other messages asking for the same thing.
4. Build relationships way before you ask for a referral.
While I donโt do many referrals bc of how many inquiries I get, Iโd be much more likely to refer someone who adds to the conversation by commenting on my posts, creates good posts themselves, and overall seems like a smart, nice person.
Doing this turns you from a complete stranger to a friend.
I know a lot of people are pressed for time on here, but building relationships is what networking is all about.
Do that effectively and your network may offer you referrals when thereโs an opening.
Join this channel for more Interview Preparation Tips: https://t.iss.one/jobinterviewsprep
ENJOY LEARNING ๐๐
1. Be personal. (never use AI)
I get a ton of messages that are either written by AI or obviously copy and pasted to 100 people.
Be personal by mentioning something you have in common with the person youโre messaging or what you got out of one of their posts.
2. Have a specific job that you want to apply for and send the link.
โCan you look and see if there are any openings?โ is incredibly rude and inconsiderate of the personโs time.
If you want them to help you with a referral, do the work for them by sending them the link, why youโre a good fit, and other needed info.
3. Reach out to people who are active on LinkedIn, but not content creators.
Everytime thereโs an opening at my company, I get 50 messages asking for a referral. As much as I want to, I canโt refer everyone.
Therefore, look for those to connect with at a company youโre interested in that post occasionally on LinkedIn, but are not content creators.
These people will be active enough to see your message, but not have 3 dozen other messages asking for the same thing.
4. Build relationships way before you ask for a referral.
While I donโt do many referrals bc of how many inquiries I get, Iโd be much more likely to refer someone who adds to the conversation by commenting on my posts, creates good posts themselves, and overall seems like a smart, nice person.
Doing this turns you from a complete stranger to a friend.
I know a lot of people are pressed for time on here, but building relationships is what networking is all about.
Do that effectively and your network may offer you referrals when thereโs an opening.
Join this channel for more Interview Preparation Tips: https://t.iss.one/jobinterviewsprep
ENJOY LEARNING ๐๐
โค5