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Here’s a detailed breakdown of critical roles and their associated responsibilities:
🔘 Data Engineer: Tailored for Data Enthusiasts
1. Data Ingestion: Acquire proficiency in data handling techniques.
2. Data Validation: Master the art of data quality assurance.
3. Data Cleansing: Learn advanced data cleaning methodologies.
4. Data Standardisation: Grasp the principles of data formatting.
5. Data Curation: Efficiently organise and manage datasets.
🔘 Data Scientist: Suited for Analytical Minds
6. Feature Extraction: Hone your skills in identifying data patterns.
7. Feature Selection: Master techniques for efficient feature selection.
8. Model Exploration: Dive into the realm of model selection methodologies.
🔘 Data Scientist & ML Engineer: Designed for Coding Enthusiasts
9. Coding Proficiency: Develop robust programming skills.
10. Model Training: Understand the intricacies of model training.
11. Model Validation: Explore various model validation techniques.
12. Model Evaluation: Master the art of evaluating model performance.
13. Model Refinement: Refine and improve candidate models.
14. Model Selection: Learn to choose the most suitable model for a given task.
🔘 ML Engineer: Tailored for Deployment Enthusiasts
15. Model Packaging: Acquire knowledge of essential packaging techniques.
16. Model Registration: Master the process of model tracking and registration.
17. Model Containerisation: Understand the principles of containerisation.
18. Model Deployment: Explore strategies for effective model deployment.
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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:
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.
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Like this post if you need more resources like this 👍❤️240 Java Interview Questions😎 #resources
240 Core Java Interview Questions.pdf5.02 KB
Why SQL is a Must-Have Skill?
If you're working with data, mastering SQL is non-negotiable! It’s the backbone of handling and making sense of vast datasets in any industry.
◆ Data at Your Fingertips
Effortlessly organize, retrieve, and manage large datasets to make informed decisions faster.
◆ Stay Organized
Use primary and foreign keys to keep your data accurate and connected across tables.
◆ Unlock Insights
Combine data from multiple sources and uncover trends using SQL's powerful query capabilities.
◆ Efficiency Matters
Optimize your databases with normalization and avoid unnecessary redundancy.
◆ Advanced Tools
From ACID transactions to optimizing with DELETE vs TRUNCATE, SQL makes sure your data is consistent and secure.
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