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You Should Know:
1. Google Data Analytics
URL: https://lnkd.in/gB6AW4cm
– Learn the basics of data analytics, including data cleaning, visualization, and interpretation.
– Practice Command: Use Python’s Pandas library to clean data:
import pandas as pd
df = pd.read_csv('data.csv')
df.dropna(inplace=True) # Remove missing values
df.to_csv('cleaned_data.csv', index=False)
2. Learn Python Basics for Data Analysis
URL: https://lnkd.in/gMkKVEWz
– Practice Command: Use Python to calculate basic statistics:
import numpy as np
data = [10, 20, 30, 40, 50]
mean = np.mean(data)
print("Mean:", mean)
3. Data Analysis with R Programming
URL: https://lnkd.in/gCEZ7b_9
– Practice Command: Use R to create a histogram:
data <- c(10, 20, 30, 40, 50) hist(data, main="Data Distribution", xlab="Values")
4. Foundations: Data, Data, Everywhere
URL: https://lnkd.in/gTNEeJbz
– Practice Command: Use Linux to count lines in a file:
wc -l data.txt
5. Ask Questions to Make Data-Driven Decisions
URL: https://lnkd.in/g6fuu49S
– Practice Command: Use SQL to query a database:
SELECT * FROM customers WHERE age > 30;
6. Process Data from Dirty to Clean
URL: https://lnkd.in/gr4sNV7H
– Practice Command: Use Python to remove duplicates:
df.drop_duplicates(inplace=True)
7. Share Data Through the Art of Visualization
URL: https://lnkd.in/gUiGt78Z
– Practice Command: Use Matplotlib to create a bar chart:
import matplotlib.pyplot as plt plt.bar(['A', 'B', 'C'], [10, 20, 30]) plt.show()
8. Analyze Data to Answer Questions
URL: https://lnkd.in/g2HHyjh5
– Practice Command: Use Python to calculate correlation:
df.corr()
9. Get Started with Python
URL: https://lnkd.in/gj8xgxNH
– Practice Command: Run your first Python script:
print("Hello, Data Analytics!")
- Go Beyond the Numbers: Translate Data into Insights
URL: https://lnkd.in/g4xH9gH5
– Practice Command: Use Linux to sort data:
sort data.txt
11. The Power of Statistics
URL: https://lnkd.in/gWAeUG-m
– Practice Command: Use Python to calculate standard deviation:
import numpy as np
data = [10, 20, 30, 40, 50]
std_dev = np.std(data)
print("Standard Deviation:", std_dev)
12. Regression Analysis: Simplify Complex Data Relationships
URL: https://lnkd.in/gr28g-QC
– Practice Command: Use Python to perform linear regression:
from sklearn.linear_model import LinearRegression model = LinearRegression() model.fit(X, y)
13. The Nuts and Bolts of Machine Learning
URL: https://lnkd.in/gM8Tk_UV
– Practice Command: Use Python to train a simple ML model:
from sklearn.datasets import load_iris from sklearn.model_selection import train_test_split from sklearn.ensemble import RandomForestClassifier iris = load_iris() X_train, X_test, y_train, y_test = train_test_split(iris.data, iris.target, test_size=0.2) model = RandomForestClassifier() model.fit(X_train, y_train)
14. Google Advanced Data Analytics Capstone
URL: https://lnkd.in/gXk-jB4e
– Practice Command: Use Linux to compress data:
tar -czvf data.tar.gz data_folder
What Undercode Say:
Data analytics is a critical skill in today’s data-driven world. These free courses from Google provide an excellent opportunity to learn and practice data analysis, visualization, and machine learning. By mastering tools like Python, R, and SQL, you can unlock insights from data and make informed decisions. Start your journey today with these resources and enhance your analytical skills!
References:
Reported By: Anish Jaitwar – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
