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Data Science: Leverage the Science of Decision-Making

Do you feel vulnerable in today’s multifaceted, volatile business environment? Get a grip on the situation with data science.

Data science offers a powerful approach to problem-solving, combining sophisticated techniques from multiple disciplines. It goes beyond traditional Business Intelligence to take advantage of modern statistical and computational modeling. In an ever-shifting business scenario, data science keeps you anchored with analytical insights into key business drivers and futuristic solutions.

Get your data science project rolling with QBurst to glean new insights and strategize decision-making.

Data Science Image
Identify Patterns Identify Patterns
Future Trends Predict Future Trends
Detect Anomalies Detect Anomalies

Our Data Science Approach to Problem-Solving

Problem Solving
Data Discovery

Study the Business DomainWe start by building a high-level understanding of your company.

Define the Business ProblemIs it about increasing click-through rates? Identifying an ideal location for your store? Whatever the objective, it is clearly framed.

Identify Data SourcesQualitative or quantitative, structured or unstructured, archived or streaming, data relevant to the analysis and their sources are identified.

Data Preparation

Data Cleansing & TransformationThe most labor-intensive step, this involves preprocessing data based on the analysis requirement. Checking for missing values, removing inconsistencies, normalizing datasets, we make sure the data is ready for exploration.

Tools: Data Wrangler, OpenRefine

Data Exploration

Exploratory Data Analysis (EDA)At this stage, we get an intuition of the data, identify important variables and their relationships, detect outliers, check assumptions, and choose relevant techniques for modeling. Graphical statistical techniques from the simple histogram and scatter plot to probability plot and seasonal subseries plot are used.

Tools: SPSS, Weka, R

Data Modeling

Based on the insights from EDA and the requirement, we develop predictive or descriptive models—from simple regression to deep learning. After multiple iterations, the most suitable model is created. These models are then validated with the test data and approved by our domain experts.

Tools: R, Python, Scikit, TensorFlow

Communication

Using statistical descriptions and visualization techniques, we communicate the key findings from the models implemented. With the help of interactive tools, business users can change the variables in the model and explore potential outcomes to strategize or refine their decisions.

Tools: Gephi, D3.js, GGobi

Application of Data Science Across Domains

Retail

  • Targeted campaigns to maximize ROI
  • Personalized recommendation systems and reward programs
  • Demand forecast-based warehouse optimization
Retail

Healthcare

  • Analysis of clinical research data
  • Improved surveillance and response to infectious diseases
  • Prescription adherence monitoring
Healthcare

Finance

  • Real-time monitoring of financial fraud
  • Design of new products/services based on past preferences
  • Customer segmentation and profiling
Finance

Education

  • Automated scoring of descriptive essays
  • Design of learning modules that adapt to learner’s skills
  • Analysis of factors affecting student failure
Education

Machine Learning in Data Science

Machine learning, with its emphasis on automatic learning, spearheads most of the newly emerging data science applications. Using powerful machine learning algorithms, you can contextualize information hidden in large data sets to address challenging problems and arrive at more intelligent decisions. Read More

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