We help companies analyse terabytes of data and extract value from it. New-age technologies such as cognitive computing, predictive modelling, and real-time data analytics are utilised to extract actionable insight when traditional data analysis methods fail. Let us decode your cryptic data and uncover insights that will keep you one step ahead of the pack.
Exploratory Data Analysis
Getting to understand data is very important and that is where EDA comes in. Once we have the data organized and imported, we use different tools and techniques to make an overall characteristic of the data. Visual graphs and charts, statistical models are mainly used to analyse the data in depth. We mainly use Power BI and Python programming language and its libraries such as Pandas, NumPy, Matplotlib, Seaborn etc for this process. The main steps that we follow in EDA are:
- Data Cleaning: It is in this step where we identify and the missing values and the outliers in the data and how we handle those situations.
- Statistical Analysis: Statistical report is generated on the numerical data in order to understand the distribution of data.
- Univariate Analysis: Data analysis is done by taking one variable at a time. It is mainly used to understand the distribution of data. Different graphs are used for analysing numerical and categorical variables.
- Multivariate Analysis: Analysis is done by considering more than one variable at a time. Box plots, Scatter plots, Density plots, correlation matrices, pair plots are some of the techniques we use to identify relations between the variables.
It is very difficult to assess the large and complex data which are available both inside and outside the organization. So, it is necessary to consolidate the data into an interpretable form in order to gain useful insights which can drive the business in the right direction. Data can be easily understood when visualized into charts and diagrams such as bar chart, doughnut chart, treemap, heat map, column chart etc.
Data visualization is performed using Power BI to get Key insights to the data and to build appealing and interactive graphs/charts using which we are able to identify the pattern and able to publish the dashboard online.
These reports ultimately help the management to understand what the data is telling and make a key business decisions based on the previous results.
Data engineering is the process of organizing the available data into a streamline for different applications of data. Data Analytics needs data organized into a single table where it can be easily connected to BI software and make interactive Dashboards. The Data Science team needs a specific set of data available to them which is used to feed an ML model and can be trained on. Hence, data engineering is an integral part of acquiring insights from the data.
Want to know About It
Data engineering is the process of organizing the available data into a streamline for different applications of data. Data Analytics needs data organized into a single table where it can be eas
AI/ML Prediction Models
A prediction model that predicts the leads’ conversion probability based on the static and interactive data. Supervised Classification models such as Logistic Regression, Random Forest, SVM, NB, Neural Networks are trained on the data and the best performing model based on the selected model evaluation metrics is qualified for deployment.
A prediction model which is used to predict whether an acquired customer might churn in the future and hence listen to the customer and take care of them. Supervised Classification models mentioned above are trained on a specific set of static, dynamic data and the best performing model is selected for deployment.
Unsupervised ML models such as K-Means Clustering, Hierarchical clustering is done on the customer data and groups are identified. The marketing team performance can be improved by changing the campaign strategies based on the identified groups.
Supervised ML model is trained on the campaign-specific data and predictions are made whether the campaign should be scaled, optimized or stopped based on the quality lead generation.
AI Model Deployment
Prediction models are developed based on the business requirements and are evaluated on case-specific evaluation metrics. The best performing model is selected and deployed into a remote server where real-time prediction is done on the incoming data and is updated into the database.