Karthik Shashidhar's presentation at the July Fintech Open Mic organised by New Finance in London

1. Leverage data to take your business to the next level with
Bespoke Data Insights
http://bespokedatainsights.com
info@bespokedatainsights.com

2. Data Science in practice
• My data is bigger than yours
• I use more predictor variables than you
• My method is more complicated than yours
• My technique is “more latest” than yours

3. Knowledge/application of machine learning
techniques is no longer a competitive advantage
Most “machine learning” techniques can be implemented using 3 very similar lines of code in
Python
import sklearn.linear_model as sklm
model = sklm.LogisticRegression()
model.fit(X_train, y_train)
Logistic Regression
from sklearn import tree
model = tree.DecisionTreeClassifier()
model.fit(X_train, y_train)
Decision Trees
from sklearn import ensemble
model = ensemble.RandomForestClassifier()
model.fit(X_train, y_train)
Random Forest
from sklearn import sam
model = svm.SVC()
model.fit(X_train, y_train)
Support Vector Machines
from sklearn.neural_network import MLPClassifier
model = MLPClassifier()
model.fit(X_train, y_train)
Neural Networks

4. The importance of a hypothesis-driven approach
• Data problem solving is no different from problem
solving
• Starting with the data can lead you on a wild goose
chase
• It is possible to do plenty of data analysis without
really solving any business problems
Start with the business problem. If you don’t have the data you
want, use proxies

5. The need to steer clear of biases
• Correlation does not imply causation
• Poor models can amplify biases implicit in
training data set
• It is important that we know, at least to
some extent, how we are solving the
problem
• Look for the “physical meaning” of the
solution, and check if it makes sense

6. The Data Diagnostic Program
• Everybody is using Machine Learning. Our investors want us to use
Machine Learning. We collect data, but don’t know where to start
• A 1-day interactive workshop
• Examine data and talk to leadership team
• Recommendations on how to make the business data-driven

7. Training programs to better leverage data science
Data Science for general managers
• Basic fundamentals of data science
• Demystifying jargon
• How to ask the right questions
Business for data scientists
• Overview of business fundamentals
• Better communication with business
• Understanding how the business side thinks
Too often, businesses fail to get the most out of their data science teams because of lack of effective communication
and coordination

8. Business consulting using data
• Classic strategy consulting, except - we’re data driven
• A hypothesis driven approach, focussed on your business problem
• End-to-end offering, from business problem to business approach
• Full range data solutions, not just restricted to machine learning
• Approach and solutions crafted for the precise business problem at hand

9. Dive in data lake
Find random correlations
Achieve Deep Learning
Karthik Shashidhar, i.e. me :)
Bespoke Data Insights Limited
http://bespokedatainsights.com
info@bespokedatainsights.com