I don’t carry my personal laptop around much. So I need a space to practice my code on the fly – somewhere online. I’ve been pursuing a cloud based python IDE for sometime. I stumbled on the datacamp site – which not only helps you around on coding – but also provides a code and execution environment to try out some stuff.
Its really cool. I need to check if they have the capability to save and integrate code on github as well.
Some learning on usage of line charts with focus on the portion highlighted in red
import matplotlib.pyplot as plt
year = [1950, 1951, 1952, …, 2100]
pop = [2.538, 2.57, 2.62, …, 10.85]
# Add more data
year = [1800, 1850, 1900] + year
pop = [1.0, 1.262, 1.650] + pop
plt.plot(year, pop)
plt.xlabel(‘Year’)
plt.ylabel(‘Population’)
plt.title(‘World Population Projections’)
#Rename y ticks
plt.yticks([0, 2, 4, 6, 8, 10],
[‘0’, ‘2B’, ‘4B’, ‘6B’, ‘8B’, ’10B’])
plt.show()
The portion highlighted in red gives you two simple things:
a. Extending the data range of what you may already have in an input dataset
b. Renaming the yticks to something else – while still retaining the range on which it is broken.
Also came across the an industry recognized certification in Data Science offered by Microsoft. Here is what they seem to cover
The course content seems to be limited whereas the UpX Academy 6 months program certification is exhaustive:
Module 1: Data Science Introduction & Use Cases
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Module 2: Python Basics
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Module 3: Python Basics
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Module 4: Statistics 1
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Module 5: Statistics 1
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Module 6: Python Advanced
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Module 7: Python Advanced
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Module 8: Exploratory Data Analysis
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Module 9: Exploratory Data Analysis
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Module 10: Exploratory Data Analysis
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Module 11: Introduction to Tableau
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Module 12: Data visualisation
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Module 13: Analytics concepts with Statistics – I
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Module 14: Analytics concepts with Statistics – II
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Module 15: Analytics concepts using calculated fields
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Module 16: Analytics concepts for integrating dashboards
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Module 17: Mini project workshop – Visual Analytics
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Module 18: Integration of Tableau with Python
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Module 19: ML Introduction & Use Cases
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Module 20: Statistics 2 – Inferential Statistics
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Module 21: Linear Regression
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Module 22: Logistic Regression
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Module 23: Decision Trees, Random Forest
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Module 24: Modelling Techniques(PCA, Feature Engineering)
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Module 25: KNN, Naive Bayes
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Module 26: Support Vector Machines(SVM)
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Module 27: Clustering, K-means
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Module 28: Time Series Modelling
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Module 29: Market Basket Analysis & Apriori Algorithm
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Module 30: Recommendation System
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Module 31: Recommendation System – Mini Project
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Module 32: Dimensionality Reduction (LDA,SVD)
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Module 33: Dimensionality Reduction (Matrix optimisation)
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Module 34: Anomaly Detection
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Module 35: XG Boost
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Module 36: Gradient Boosting Machine(GBM)
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Module 37: Stochastic Gradient Descent(SGD)
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Module 38: Ensemble Learning – I
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Module 39: Ensemble Learning – II
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Module 40: Introduction to Neural Networks
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Module 41: Introduction to NLP & Deep Learning
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Module 42: Word Embeddings
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Module 43: Word window classification
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Module 44: Introduction to Artifcial Neural Networks
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Module 45: Introduction to Tensorflow
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Module 46: Recurrent Neural Networks for Language modelling
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Module 47: Gated Recurrent Units(GRUs), LSTMs
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Module 48: Recursive Neural network
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Module 49: Convolutional Neural Networks for sentence classification
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Module 50: Dynamic Memory Networks
