Datacamp, Microsoft Data Science Cert and UpXAcademy

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 coverMicrosoft Data Science

The course content seems to be limited whereas the UpX Academy 6 months program certification is exhaustive:

Module 1: Data Science Introduction & Use Cases 0/1

  • Leason1.1
    Fundamentals,Usecases
  • Module 2: Python Basics 0/2

    • Leason2.1
      Basic Syntax
    • Leason2.2
      Data Structures
  • Module 3: Python Basics 0/4

    • Leason3.1
      Loops
    • Leason3.2
      If-elif statements
    • Leason3.3
      Functions
    • Leason3.4
      Exception Handling
  • Module 4: Statistics 1 0/3

    • Leason4.1
      Measures of central tendency
    • Leason4.2
      Population
    • Leason4.3
      Sample, Probability Distribution
  • Module 5: Statistics 1 0/3

    • Leason5.1
      Normal and Binomial Distribution
    • Leason5.2
      Random Variable
    • Leason5.3
      Pictorial Representations
  • Module 6: Python Advanced 0/2

    • Leason6.1
      Numpy
    • Leason6.2
      Pandas
  • Module 7: Python Advanced 0/2

    • Leason7.1
      Data Manipulation
    • Leason7.2
      Matplotlib
  • Module 8: Exploratory Data Analysis 0/2

    • Leason8.1
      Data Cleaning
    • Leason8.2
      Data Wrangling
  • Module 9: Exploratory Data Analysis 0/1

    • Leason9.1
      Data Visualisation
  • Module 10: Exploratory Data Analysis 0/1

    • Leason10.1
      Case Study
  • Module 11: Introduction to Tableau 0/1

     
  • Module 12: Data visualisation 0/1

  • Module 13: Analytics concepts with Statistics – I 0/1

  • Module 14: Analytics concepts with Statistics – II 0/1

     
  • Module 15: Analytics concepts using calculated fields 0/1

     
  • Module 16: Analytics concepts for integrating dashboards 0/1

     
  • Module 17: Mini project workshop – Visual Analytics 0/1

  • Module 18: Integration of Tableau with Python 0/1

    • Leason18.1
      lessons will be updated soon
  • Module 19: ML Introduction & Use Cases 0/3

    • Leason19.1
      ML Intro
    • Leason19.2
      Fundamentals
    • Leason19.3
      Use Cases
  • Module 20: Statistics 2 – Inferential Statistics 0/1

    • Leason20.1
      lessons will be updated soon
  • Module 21: Linear Regression 0/1

    • Leason21.1
      lessons will be updated soon
  • Module 22: Logistic Regression 0/1

    • Leason22.1
      lessons will be updated soon
  • Module 23: Decision Trees, Random Forest 0/1

    • Leason23.1
      lessons will be updated soon
  • Module 24: Modelling Techniques(PCA, Feature Engineering) 0/1

    • Leason24.1
      lessons will be updated soon
  • Module 25: KNN, Naive Bayes 0/1

    • Leason25.1
      lessons will be updated soon
  • Module 26: Support Vector Machines(SVM) 0/1

    • Leason26.1
      lessons will be updated soon
  • Module 27: Clustering, K-means 0/1

    • Leason27.1
      lessons will be updated soon
  • Module 28: Time Series Modelling 0/1

    • Leason28.1
      lessons will be updated soon
  • Module 29: Market Basket Analysis & Apriori Algorithm 0/1

    • Leason29.1
      lessons will be updated soon
  • Module 30: Recommendation System 0/1

    • Leason30.1
      lessons will be updated soon
  • Module 31: Recommendation System – Mini Project 0/1

    • Leason31.1
      lessons will be updated soon
  • Module 32: Dimensionality Reduction (LDA,SVD) 0/1

    • Leason32.1
      lessons will be updated soon
  • Module 33: Dimensionality Reduction (Matrix optimisation) 0/1

    • Leason33.1
      lessons will be updated soon
  • Module 34: Anomaly Detection 0/1

    • Leason34.1
      lessons will be updated soon
  • Module 35: XG Boost 0/1

    • Leason35.1
      lessons will be updated soon
  • Module 36: Gradient Boosting Machine(GBM) 0/1

    • Leason36.1
      lessons will be updated soon
  • Module 37: Stochastic Gradient Descent(SGD) 0/1

    • Leason37.1
      lessons will be updated soon
  • Module 38: Ensemble Learning – I 0/1

    • Leason38.1
      lessons will be updated soon
  • Module 39: Ensemble Learning – II 0/1

    • Leason39.1
      lessons will be updated soon
  • Module 40: Introduction to Neural Networks 0/1

    • Leason40.1
      lessons will be updated soon
  • Module 41: Introduction to NLP & Deep Learning 0/1

    • Leason41.1
      lessons will be updated soon
  • Module 42: Word Embeddings 0/1

    • Leason42.1
      lessons will be updated soon
  • Module 43: Word window classification 0/1

    • Leason43.1
      lessons will be updated soon
  • Module 44: Introduction to Artifcial Neural Networks 0/1

    • Leason44.1
      lessons will be updated soon
  • Module 45: Introduction to Tensorflow 0/1

    • Leason45.1
      lessons will be updated soon
  • Module 46: Recurrent Neural Networks for Language modelling 0/1

    • Leason46.1
      lessons will be updated soon
  • Module 47: Gated Recurrent Units(GRUs), LSTMs 0/1

    • Leason47.1
      lessons will be updated soon
  • Module 48: Recursive Neural network 0/1

    • Leason48.1
      lessons will be updated soon
  • Module 49: Convolutional Neural Networks for sentence classification 0/1

    • Leason49.1
      lessons will be updated soon
  • Module 50: Dynamic Memory Networks 0/1

    • Leason50.1
      lessons will be updated soon
Advertisements

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: