1) Data analytics involves treating available digital data as a "gold mine" to obtain tangible outputs that can improve business efficiency when applied. Machine learning uses algorithms to correlate parameters in data and improve relationships. 2) The document provides an overview of getting started in data science, covering business objectives, statistical analysis, programming tools like R and Python, and problem-solving approaches like supervised and unsupervised learning. 3) It describes the iterative "rule of seven" process for data science projects, including collecting/preparing data, exploring/analyzing it, transforming features, applying models, evaluating performance, and visualizing results.

1. FIRST STEPS IN DATASCIENCE
Tips and tools for wannabe data analysts
By Sheshachalam Ratnala

2. Data analytics Aka Machine Learning
Data analytics as an area
where the available digital
data is treated as a Gold
Mine from where tangible
output is obtained which
when applied impacts
businesses and it’s
efficiency.
Machine Learning is the
tool in the form of y=f(x)
which co-relates all the
parameters in the data to
obtain the relation which it
learns from these
parameters and keeps on
improving the relationship
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3. Data analytics Aka Machine Learning`
Data : It is a set of values of quantitative and qualitative
variables. Historic information or knowledge represented
in usable form
Population - Entire group
It’s the collection of data which represents whole of the problem domain
Sample - A portion of the group
Subset of the population to be taken for inference which is the true representation
of the overall population
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4. Data analytics – How to start
Data Science/Data analytics With what ever name it’s
been known to you has essentially 3 areas to cover
Business
StatisticsProgramming
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5. Data analytics – How to start
Business – Critical thinking
1. Objective analysis and evaluation of an issue in order to form a judgement
2. This is the stage to build the hypothesis for the problem domain in context
3. The model below could be a way to follow
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6. Data analytics – How to start
Statistics – Mathematical Analysis
Data is considered as variable and the hierarchy is as follows
Data
(Variables)
Numerical
(Quantitative)
Discrete Continuous
Categorical
(Qualitative)
Ordinal
(Logically
ordered)
Nominal
(Unordered)
Continuous
Any values between a permitted
range(5.3, 5.35,5.45 6.0)
Discrete
Whole no: 5, 10
Ordinal
Logical order like Low; Med; High
Nominal
Male ;Female , Different types of 4
wheelers
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7. Data analytics – How to start
Programming - Execution
R is the widely used tool due it’s historical
statistical usage and it’s abundant statistical
libraries
Python the interpreted language provides
a wide variety of packages for application
development and it’s statistical library .
Data ingestion Tools: Spark, Hadoop
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8. Data analytics – Problem perspective
Solution
Hypothesis
Supervised
Learning
Numerical Data
(Target Variable)
Regression
Linear Regression Time Series
Categorical data
(Target Variable)
Classification
Decision Trees Random Forest K NN Logistic
Regression
Demand
Forecasting
Reinforcement
learning
Semi-Supervised
NLP and AI
Unsupervised
Clustering
K Means Hierarchical
clustering
Dimensionality
Reduction
Collaborative
filtering
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Classifying the problem

9. Data analytics – Problem Complexity
The solution
complexity
and data
volume
increases
with the
kind of
business
value being
generated
Credits : odoscope: Overview of analytics methods
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10. Data analytics – The execution
Basic Terminology
• Attribute - Features are a quantitative attributes of the samples
being observed
• Axis - Features are orthogonal axes of their feature space, if
they are linearly independent
• Column/Independent variables - Features are represented as
columns in your dataset
• Dimension - A dataset's features, grouped together can be
treated as a n-dimensional coordinate space
• Input - Feature values are the input of data-driven, machine
learning algorithms
• Predictor/Dependent variable - Features used to predict other
attributes are called predictors
• View - Each feature conveys a quantitative trait or perspective
about the sample being observed
• Independent Variable - Autonomous features used to calculate
others are like independent variables in algebraic equations
Structuring the data
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11. Data analytics – The execution
The rule of Seven
The steps are iterative at any stage
• Data collection(Problem context)
• Data Wrangling/Data Munging(Data cleaning)
• Data exploring/Analysis
• Data Transforming
• Modelling
• Model evaluation
• Data Visualization( Intelligence)
The machine learning models works only on clean structured data . 5 out of 7 steps are
related to pre-processing of the data given to model.
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12. Data analytics – The execution
1. Data collection /selection
1.No bias in the data feature
2.Relevant data feature
3.Techniques to handle
a) Data Collection:
1. Data from sources related to problem i..e DB’s,Weblogs,emails etc..
2. Any audio,video,sensor data etc .
3. The 6 Vs of data , Variety ,Velocity,Verasity,Volume,Value,Viable
b) Data Selection:
1. PCA : Unsupervised data
2.LDA (Linear discrimant analysis) : Supervised data
The rule of Seven
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13. Data analytics – The execution
2. Data cleaning (Garbage in Garbage Out)
1. Data obtained is not clean and have below issues:
1. Outliers 4. Erroneous data7. Need formatting
2. Missing data 5. Irrelevant data
3. Malicious data 6. Inconsistent data
2. Techniques to handle
1.Impute values by Mean ,Median or Mode
2. Treat outliers by deleting the row if not at all related else analyze with more data
3.Binning
4.Creating new features from given features
5.Dummy variables
The rule of Seven
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14. Data analytics – The execution
3. Data Analysis (Data exploring)
1.Find the relevance of the feature set. Apply all the basic statistical exploration i..e moments
2. Obtain the statistical relation.
3.Perform basic visualizations for obtaining the concrete feature set.
4.Techniques to handle
1.Univariate analysis ( Mean ,mode, Normal distrubution,Variance,Skewness,Kurtosis)
2.Bi-Variate analysis ( Scatter plot, Box plot, Histogram)
3.Multi-variate analysis (Probability distribution functions PDFs)
The rule of Seven
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15. Credits: https://jixta.wordpress.com/
Data analytics – The execution
Data analysis – Adopt few basics visualizations from the list
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16. Data analytics – The execution
4. Data Transformation(Data on the same scale)
1. Ensure that the rest of the features are informative and transformation changes the no. of features or
the feature values. This is also known as Feature engineering
2. Dimensionality Reduction
3. Curse of dimensionality
4. Techniques to handle
1.PCA : Principal component analysis
2.Kernel Trick
3.Normalization
The rule of Seven
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17. Data analytics – The execution
6. Machine learning modeling
1. Split data as Test , Train.
2. Keep some data never tested or get
some sample termed as “out of sample”
3. Apply the appropriate ML algorithm on the train data.
4.Check the accuracy with the test data .
5.Observer the Bias and Variance
a)Bias is how far is the target value w.r.t actual value
b)Variance is how distributed is the value w.r.t actual value
c)Error = variance + Bias²
The rule of Seven
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18. Data analytics – The execution
The rule of Seven
6.1 Machine learning modeling
2.Apply the appropriate algorithm
as described by solution hypothesis
Ref: cheatsheet
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19. Data analytics – The execution
6.2 Machine learning model
1. Model Performance
1. Model validation
1. MSE ( Mean square error) 2. Hypothesis testing 3.Cross-validation
2. Algorithm tuning
1.Tuning the co-efficient parameters 2..Increasing the splits
3. Feature engineering (iterate again for features)
4. Cross validation
1. K-Fold
5. Ensemble method ( Combining the ML algorithms)
1. Voting ( Selection based on voting on performance) 2.Bagging( Bootstrapping + Aggregating) 3.Boosting (Weak learner
to strong learner.
The rule of Seven
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20. Data analytics Aka Machine Learning
6.3.1 Machine learning model performance
1. Confusion matrix ( Hypothesis testing
Measurement terms
1. Precision 3.Accuracy 5.False positive(Fallout-rate)
2. Recall 4.Specificity 6.False negative (Miss rate)
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The rule of Seven

21. Data analytics Aka Machine Learning
6.3.2 Machine learning model performance
1. Cross-fold validations
• Random division of data sets
• ML algorithm check for each
subset
• Overall efficiency as the final
accuracy of the model
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The rule of Seven

22. Data analytics Aka Machine Learning
7. Data Visualization
1. Storifying the data analysis as Descriptive ,prescriptive or predictive
2. Effective use of the visuals graphs.
3.Tools like Tableau ,D3.js ,Matplotlib,chart.js
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The rule of Seven

23. Data analytics Aka Machine Learning
Tools in practice
Core – Python library
NumPy
Pandas
Matplotlib
Scikit-learn
(Machine learning algos)
(Mathematical computing functions /N- Dimensional array )
(Data Analysis ,Data munging by in
memory data representation) (2 D Visualization library)
For a high level language user python is the best tool available to use
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24. Data analytics Aka Machine Learning
Tools sources
1. Anaconda
1. Use IPython universal editor
2. Python 2.7+ or 3.5
3. Careful about the version because of supporting function
4. A good starting tool
5. Spyder Interactive editor tool for basic python learning
2. Enthought Canopy.
1. Interactive environment
3. Pycharm by jetbrains : Interactive IDE debugger tool
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25. Data analytics Aka Machine Learning
Tools cheat sheets
Must visit sites
KdNuggets
Kaggle
DatascienceCentral
DataCamp
https://www.class-central.com/
http://analyticsvidhya.com/
https://www.odsc.com/
http://www.pythonlearn.com/
http://datascienceplus.com/
Practice data sets
http://ipython-books.github.io/minibook/
http://learnds.com/
https://vincentarelbundock.github.io/Rdatasets/
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26. Thank you !!!
Connect with me at
r.shera01@gmail.com
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