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Cluster Analysis in Data Mining: Applications, Methods & Requirements [With Examples]

Updated on 08 November, 2024

115.8K+ views
16 min read

As technology grows, so does the amount of data we create. According to Statista, global data creation is expected to exceed 180 zettabytes by 2025. With so much data, cluster analysis in data mining can help.

Cluster analysis organizes data by grouping similar items together. It helps us find patterns and sort information more easily. It is similar to putting things into different boxes based on their common characteristics.

Cluster analysis in data mining is used in many areas:

  • Businesses use it to group customers with similar buying habits.
  • Image processing tools use it to separate parts of an image.
  • Social media platforms use it to recommend content to users.

In this blog, we’ll go through the basics of cluster analysis, look at how it’s used, and explain the main methods and tools. Real-life examples will show how clustering helps in organizing data and finding patterns that are easy to understand.

Check out: Common Examples of Data Mining.

What is a Cluster?

A cluster is a way to group similar things together and keep different things separate. Clusters help organize and categorize items based on what they have in common. For example, imagine sorting a mix of fruits and vegetables. You might group all the vegetables together in one cluster and all the fruits in another. In this case, fruits and vegetables are two clusters, each holding similar items. 

What is Clustering in Data Mining?

Clustering in data mining is a technique used to divide data into meaningful subgroups or “clusters.” This method helps organize large datasets by identifying patterns and similarities among data points, even without predefined labels.

Clustering is a type of unsupervised learning because it doesn’t require known labels or categories in advance. Instead, clustering finds patterns on its own by grouping similar data points together based on shared characteristics.
 


Properties of Clustering

Clustering is useful for finding patterns without needing prior knowledge of class labels. Good clustering relies on key properties that define how well the data is grouped. Here’s a breakdown of these essential properties:

  • Homogeneity: Data points within each cluster are similar to each other. This similarity gives each cluster a unique set of shared characteristics.
  • Separation: Different clusters are distinct from one another, with clear boundaries. Good separation keeps clusters from overlapping and makes it easier to analyze each group.
  • Compactness: Data points in each cluster are close together. This forms tight, well-defined groups that are easy to interpret.
  • Connectedness: Points within a cluster are more closely connected to each other than to points in other clusters. This creates strong relationships within each group.

Different clustering methods focus on these properties in different ways. For example:

  • K-Means Clustering groups data points around a defined number of central points, which creates compact clusters.
  • Hierarchical Clustering gradually builds clusters in a layered structure, making groups that reflect natural connections within the data.
  • Density-Based Clustering focuses on dense areas of data and creates clusters that are compact and separated.

Property

Description

Homogeneity

Points in the same cluster are similar, so each cluster has clear, shared traits.

Separation

Different clusters are distinct and help avoid overlap between clusters.

Compactness

Points in a cluster are close together, which forms tight, well-defined groups.

Connectedness

Points are more connected within clusters than to points in other clusters, which maintains cohesion.

Importance of Cluster Analysis in Data Mining

Cluster analysis helps make sense of large sets of data by grouping similar data points together. This approach reveals patterns and insights that may not be obvious at first glance. Here’s why cluster analysis is valuable:

  • Customer Segmentation: Around 90% of companies use clustering to understand their customers better. It helps them group customers by shopping habits, interests, or preferences. This way, businesses can create offers that match each group’s needs.
  • Market Research: Clustering helps businesses spot specific groups in a market, like age groups or regional preferences. With this, they can design products or services that speak directly to each group.
  • Image and Visual Recognition: In healthcare, clustering is used to highlight key areas in medical images, like identifying organs in a scan. It also helps in apps that recognize faces or objects, making image processing faster and more accurate.
  • Detecting Unusual Patterns: Clustering helps find unusual data points. For example, banks use it to spot suspicious transactions that don’t fit typical patterns, helping them detect potential fraud.
  • Simplifies Large Data Sets: Clustering organizes data into smaller, related groups. This makes it easier to analyze and keeps analysts from getting lost in too much detail.
  • Social Media Recommendations: Social platforms use clustering to group users by common interests. This makes it easy to suggest friends, groups, or content that each user might enjoy.

Clustering Methods in Data Mining

Clustering methods in data mining are used to group data points into meaningful clusters. Each method has a different approach, suitable for various types of data and clustering goals. Here’s an overview of key clustering methods:

Clustering Method

Key Features

When to Use

Partitioning (K-Means)

Groups data into a set number (k) of clusters based on closeness to cluster centers (centroids).

Best for large datasets with clear, separate clusters, like dividing customers by spending habits.

Hierarchical (Agglomerative & Divisive)

Builds clusters step-by-step, either by merging or splitting, without needing a set number of clusters.

Great for organizing data into levels, like customer groups based on purchase history.

Density-Based (DBSCAN)

Clusters based on dense areas of data points; handles uneven shapes and noise well.

Ideal for data with outliers or complex shapes, like mapping natural clusters in geographic data.

Grid-Based

Divides data space into grids, clustering each cell separately; quick processing time.

Works well for spatial data, like detecting patterns in traffic flow across different regions.

Model-Based (Gaussian Mixture Model - GMM)

Assumes each cluster follows a statistical pattern (like a Gaussian); finds the number of clusters on its own.

Useful when data naturally follows distributions, like in biological or financial data analysis.

Constraint-Based

Clusters based on user-set rules (e.g., minimum size or distance); customizes clustering results.

Useful when specific clustering rules are needed, like grouping items based on strict guidelines.

1. Partitioning Method

The Partitioning Method divides the dataset into non-overlapping clusters or subsets. Each data point belongs to only one cluster, and there are no empty clusters. This method works by iteratively moving data points between clusters to improve clustering quality.

Characteristics:

  • Requires the number of clusters (k) to be specified in advance.
  • Ensures each data point belongs to a single cluster.
  • Uses iterative relocation to optimize clusters, meaning data points are moved between clusters to improve the quality of clustering.

Example AlgorithmK-Means Clustering

  • K-Means divides data into k clusters by finding k centroids (cluster centers) and assigning each data point to the nearest centroid. This process continues until the centroids stabilize, creating well-defined clusters based on proximity to each centroid.

Python Code Example:

This example uses K-Means clustering to divide the data into two clusters.

from sklearn.cluster import KMeans
import numpy as np

# Sample data
data = np.array([[1, 2], [2, 3], [4, 5], [6, 8], [7, 9]])

# Applying K-means with 2 clusters
kmeans = KMeans(n_clusters=2, random_state=0).fit(data)

# Output cluster labels and centroids
print("Cluster Labels:", kmeans.labels_)
print("Centroids:", kmeans.cluster_centers_)

 

Output:

lua

Cluster Labels: [1 1 1 0 0]
Centroids: [[6.5 8.5]
            [2.33333333 3.33333333]]

In this example, data points are divided into two clusters, with the centroids located at approximately [6.5, 8.5] and [2.33, 3.33].

2. Hierarchical Method

The Hierarchical Method creates a tree-like structure (or dendrogram) of clusters by progressively merging or splitting clusters. This method does not require a predefined number of clusters, and the process continues until each data point is either in its own cluster or all points form a single cluster.

Types of Hierarchical Clustering:

  • Agglomerative Approach (Bottom-Up): Starts with each data point as its own cluster, then repeatedly merges the closest clusters until a stopping criterion is reached.
  • Divisive Approach (Top-Down): Begins with all data points in a single cluster and splits clusters recursively until each data point is isolated or a stopping criterion is met.

Advantages:

  • No need to specify the number of clusters in advance.
  • Suitable for datasets where hierarchical relationships between data points are of interest.

Python Code Example:

This example performs hierarchical clustering and generates a dendrogram to visualize the clustering structure.

from scipy.cluster.hierarchy import dendrogram, linkage
import matplotlib.pyplot as plt
import numpy as np

# Sample data
data = np.array([[1, 2], [2, 3], [4, 5], [6, 8], [7, 9]])

# Perform hierarchical clustering using 'ward' linkage method
Z = linkage(data, method='ward')

# Plot dendrogram
plt.figure(figsize=(8, 4))
dendrogram(Z)
plt.title('Hierarchical Clustering Dendrogram')
plt.xlabel('Data Points')
plt.ylabel('Distance')
plt.show()

Output (Dendrogram Plot): The dendrogram visually represents the hierarchical clustering process, showing how clusters are merged based on distance. The plot displays data points on the x-axis and the merging distance on the y-axis, with clusters joined at different heights.

3. Density-Based Method

The Density-Based Method identifies clusters based on the density of data points. In this method, clusters are formed where data points are closely packed together, separated by areas of lower data density. This method is effective for discovering clusters of varying shapes and identifying outliers.

Example AlgorithmDBSCAN (Density-Based Spatial Clustering of Applications with Noise)

  • DBSCAN forms clusters based on two parameters: epsilon (the maximum distance between points in a cluster) and min_samples (the minimum number of points required to form a dense region).
  • Points in dense regions are grouped as clusters, while points that don’t meet the density requirement are marked as noise (outliers).

Advantages:

  • Does not require the number of clusters to be specified beforehand.
  • Effective for datasets with noise and irregularly shaped clusters.

Python Code Example:

This example applies DBSCAN to detect clusters based on density, with a sample dataset that includes some noise.

from sklearn.cluster import DBSCAN
import numpy as np

# Sample data
data = np.array([[1, 2], [2, 3], [4, 5], [6, 8], [7, 9], [10, 10]])

# Applying DBSCAN
dbscan = DBSCAN(eps=2, min_samples=2).fit(data)

# Output cluster labels
print("Cluster Labels:", dbscan.labels_)

Output:

Cluster Labels: [0 0 0 1 1 -1]

In this example, DBSCAN forms two clusters (labeled 0 and 1). The point [10, 10] is labeled -1, indicating it is an outlier (noise) because it doesn’t meet the density requirements to join a cluster.

4. Grid-Based Method

The Grid-Based Method divides the data space into a grid structure, where each cell represents a specific area of the data space. Clustering is then performed on these cells rather than on individual data points. This method is especially useful for spatial data, as it organizes data points based on their spatial location.

Key Points:

  • Grid Structure: Data space is quantized into a finite number of cells that form a grid.
  • Faster Processing: Since clustering is based on cells rather than individual points, the process is faster and depends on the number of cells in the grid, not on the dataset size.

Advantages:

  • Quick processing time, as it clusters based on the grid cell density.
  • Suitable for handling large spatial datasets.

Python Code Example:

Here’s a basic example demonstrating how grid-based clustering might look with a simplified grid approach. This is not a built-in grid-based clustering function, but rather a way to represent a grid-based approach.

import numpy as np
import matplotlib.pyplot as plt

# Sample spatial data
data = np.array([[1, 2], [2, 3], [4, 5], [10, 10], [12, 14], [13, 13]])

# Defining grid parameters
x_bins = np.linspace(min(data[:,0]), max(data[:,0]), 4)  # Divide x-axis into 3 bins
y_bins = np.linspace(min(data[:,1]), max(data[:,1]), 4)  # Divide y-axis into 3 bins

# Assign each point to a grid cell
x_indices = np.digitize(data[:,0], x_bins) - 1
y_indices = np.digitize(data[:,1], y_bins) - 1

# Visualize grid clustering
plt.scatter(data[:,0], data[:,1], c="blue")
plt.vlines(x_bins, min(data[:,1]), max(data[:,1]), colors="gray", linestyles="dotted")
plt.hlines(y_bins, min(data[:,0]), max(data[:,0]), colors="gray", linestyles="dotted")
plt.title("Grid-Based Clustering Example")
plt.xlabel("X-Axis")
plt.ylabel("Y-Axis")
plt.show()

# Output each point's assigned grid cell
for i, (x_idx, y_idx) in enumerate(zip(x_indices, y_indices)):
    print(f"Point {data[i]} is in grid cell ({x_idx}, {y_idx})")

5. Model-Based Method

The Model-Based Method assumes that each cluster follows a statistical model, often using Gaussian Mixture Models (GMM). This method finds clusters based on the probability that data points belong to a specific model, such as a Gaussian distribution. It also adjusts for noise and can determine the optimal number of clusters based on statistical measures.

Key Points:

  • Assumes a Model: Uses a predefined model (e.g., Gaussian) for each cluster.
  • Flexibility with Noise: Can account for noise and identify outliers.
  • Automatic Cluster Count: Determines the number of clusters based on model parameters and data distribution.

Python Code Example:

from sklearn.mixture import GaussianMixture
import numpy as np

# Sample data
data = np.array([[1, 2], [2, 3], [4, 5], [10, 10], [12, 14], [13, 13]])

# Applying Gaussian Mixture Model with 2 clusters
gmm = GaussianMixture(n_components=2, random_state=0).fit(data)

# Predict cluster labels
labels = gmm.predict(data)
print("Cluster Labels:", labels)
print("Means of Each Cluster:", gmm.means_)

Output:

lua

Cluster Labels: [1 1 1 0 0 0]
Means of Each Cluster: [[11.66666667 12.33333333]
                        [2.33333333 3.33333333]]

This example shows how Gaussian Mixture Models (GMM) cluster the data into two groups, with calculated cluster centers based on the mean of each group.

Must read: Learn excel online free!

6. Constraint-Based Method

The Constraint-Based Method incorporates user-specified constraints to guide the clustering process. Constraints can include minimum cluster size, distance thresholds, or other specific properties required for the clusters. This approach is often used in applications where domain knowledge is crucial for meaningful clustering.

Key Points:

  • User Constraints: Clustering is based on user-defined requirements, such as specific distance functions or cluster sizes.
  • Interactive Approach: Allows users to adjust clustering parameters based on their needs.
  • Application-Specific: Useful for applications requiring precise control over clustering results.

Example Scenario (Conceptual Example with Custom Constraints): Here’s a simplified conceptual example to demonstrate constrained clustering. Note that there’s no built-in function for constraint-based clustering, but custom constraints can be applied.

python

import numpy as np
from sklearn.cluster import KMeans

# Sample data
data = np.array([[1, 2], [2, 3], [4, 5], [6, 7], [10, 12], [12, 14]])

# Custom constraint: Minimum cluster size of 3
min_cluster_size = 3

# Applying K-Means with 2 clusters, then filtering by custom constraint
kmeans = KMeans(n_clusters=2, random_state=0).fit(data)
labels = kmeans.labels_

# Check cluster sizes
cluster_counts = np.bincount(labels)
valid_clusters = [i for i, count in enumerate(cluster_counts) if count >= min_cluster_size]

# Output valid clusters based on the minimum size constraint
print("Cluster Labels:", labels)
print("Cluster Counts:", cluster_counts)
print("Valid Clusters (min size 3):", valid_clusters)

Output:

Cluster Labels: [1 1 1 0 0 0]
Cluster Counts: [3 3]
Valid Clusters (min size 3): [0, 1]

This example applies a basic constraint, filtering clusters to only include those meeting the minimum size. Custom constraints can be adapted based on application needs.

Business Applications of Cluster Analysis

Cluster analysis in data mining is widely used across industries, with some impressive results. Here’s how different sectors apply cluster analysis to achieve their goals:

Industry

Application

Description

Marketing

Customer Segmentation

Groups customers by behavior, boosting personalized marketing and sales.

Targeted Advertising

Clusters users by browsing habits to create relevant, higher-conversion ads.

Healthcare

Patient Grouping

Groups patients with similar conditions to enable targeted treatment plans.

Disease Tracking

Monitors disease spread by clustering affected areas for quicker response.

Finance and Banking

Fraud Detection

Identifies unusual transaction patterns to quickly spot potential fraud.

Risk Assessment

Groups clients by risk level, helping banks make informed lending decisions.

Retail and E-Commerce

Product Recommendation

Clusters products often bought together to encourage cross-selling.

Inventory Management

Predicts product demand by region, optimizing stock levels.

Social Media

User Engagement

Clusters users by interests to recommend relevant content and connections.

Sentiment Analysis

Analyzes customer feedback clusters to understand sentiment and improve service.

Read more about the applications of data science in finance industry.

Why Validate Clusters?

Validation helps confirm that clusters provide real insights rather than random groupings. This step checks for quality, ensuring that clusters are well-formed and distinct from one another.

Types of Cluster Validation

  1. Internal Validation
    • Purpose: Measures the quality of clusters without using external or labeled data. Internal validation helps determine how well-defined and distinct clusters are based solely on the dataset.
    • How It Works: Internal validation uses metrics like compactness, separation, and connectivity to evaluate clusters.
    • Limitation: Internal validation can show relative quality but does not confirm how accurately clusters reflect real categories, as it does not use external labels.
  2. External Validation
    • Purpose: Compares clusters to a known labeled dataset to check for accuracy.
    • How It Works: Applies clustering to a labeled dataset and compares the clusters formed to the actual categories. This method helps determine how well the clustering reflects true groupings in the data.
    • Limitation: External validation relies on labeled data, which isn’t always available and might favor certain clustering methods over others.

Key Metrics for Cluster Validation

Metric

Description

Ideal Score

Silhouette Score

Measures compactness and separation of clusters

Closer to 1 (good clustering)

Dunn Index

Ratio of minimum inter-cluster to maximum intra-cluster distance

Higher value (strong separation)

Example of Clustering Algorithm

In this example, we’ll implement K-Means Clustering from start to finish using Python. This walk-through will explain each step, from preparing the data to interpreting the output.

  • Step 1: Import Libraries

First, we’ll import the necessary libraries: numpy for handling arrays and KMeans from sklearn.cluster for the K-Means algorithm.

python

import numpy as np
from sklearn.cluster import KMeans
import matplotlib.pyplot as plt
  • Step 2: Prepare the Data

For this example, we’ll use a simple dataset of points to be clustered. Each data point has two features.

python

# Sample data (X, Y coordinates)
data = np.array([
    [1, 2], [2, 3], [3, 4], [8, 8], [8, 9], [25, 80], [24, 79], [25, 77]
])
  • Step 3: Set Up and Fit the K-Means Model

We’ll create an instance of KMeans with n_clusters=3, meaning we want to split the data into 3 clusters. Then, we fit the model to our data.

python

# Initialize K-Means with 3 clusters
kmeans = KMeans(n_clusters=3, random_state=0)

# Fit the model to the data
kmeans.fit(data)
  • Step 4: Get the Cluster Labels and Centroids

After fitting the model, we can retrieve the cluster labels for each data point and the centroids of the clusters.

python

# Get cluster labels for each data point
labels = kmeans.labels_

# Get coordinates of cluster centroids
centroids = kmeans.cluster_centers_

print("Cluster Labels:", labels)
print("Cluster Centroids:", centroids)

Output:

lua

Cluster Labels: [0 0 0 1 1 2 2 2]
Cluster Centroids: [[ 2.         3.        ]
                    [ 8.         8.5       ]
                    [24.66666667 78.66666667]]

 

  • Cluster Labels: Each data point is assigned a label indicating which cluster it belongs to (0, 1, or 2).
  • Cluster Centroids: The coordinates of the centers of each cluster.
     
  • Step 5: Visualize the Clusters

To better understand the results, we can plot the data points, their assigned clusters, and the centroids.

python

# Plot data points with colors based on cluster label
plt.scatter(data[:, 0], data[:, 1], c=labels, cmap='viridis', label='Data Points')
plt.scatter(centroids[:, 0], centroids[:, 1], s=200, c='red', marker='X', label='Centroids')

plt.title("K-Means Clustering")
plt.xlabel("Feature 1")
plt.ylabel("Feature 2")
plt.legend()
plt.show()

Explanation of Results

  • Data Points: Each point is colored according to its cluster label, showing which points belong together.
  • Centroids: The red "X" markers represent each cluster's centroids, or centers. Points are grouped around their respective centroids, showing the natural separation of the data.

Advantages of Cluster Analysis in Data Mining

  • Clustering reveals hidden patterns and groups in data, making it easier to understand.
  • Clusters can help predict trends and behaviors based on historical groupings.
  • Businesses can use clustering to target customer groups effectively, improving marketing and personalization.
  • Clustering supports decision-making in areas like risk assessment and targeted advertising by clarifying key groups.
  • Clustering reduces data complexity by organizing large datasets into manageable groups for analysis.

Disadvantages of Cluster Analysis in Data Mining

  • Clustering results can vary, leading to subjective interpretations depending on the method used.
  • Some methods, like K-Means, require pre-set parameters (e.g., number of clusters) that can affect outcomes.
  • Clustering may struggle with outliers or noisy data, which can distort results.

How Companies Use Cluster Analysis to Make Better Predictions

Cluster analysis helps companies find patterns and make informed predictions. Here’s how it’s used in different industries:

  • Retail Industry: Improving Customer Segmentation

    • Intro: Retailers want to personalize marketing to boost sales and customer loyalty.
    • Challenge: With large customer bases, sending generic offers reduces engagement.
    • Solution: Clustering groups customers by buying habits and preferences. 75% of retail companies use clustering to improve customer segmentation, which enables personalized offers that drive higher engagement.
  • Healthcare: Grouping Patients by Medical Histories

    • Intro: Healthcare providers aim to identify at-risk patients to improve care.
    • Challenge: High readmission rates strain hospital resources.
    • Solution: Clustering groups patients with similar health profiles, which makes it easier to spot those at risk. This approach has helped hospitals lower readmissions by focusing on targeted care.
  • Financial Services: Identifying High-Risk Customers

    • Intro: Banks need accurate credit assessments to avoid defaults.
    • Challenge: Traditional credit scores don’t always capture customer risk accurately.
    • Solution: Clustering identifies high- and low-risk customers by income, spending, and credit history. Banks have reduced loan defaults by 15% by using clustering to refine risk models.

Master Cluster Analysis with upGrad’s Data Science Program

Learn cluster analysis, real-world applications, and essential tools for a data-driven career.

What You’ll Learn

  • Clustering Basics: Techniques like Partitioning, Hierarchical, and Density-Based Clustering.
  • Hands-On Projects: Work with Python and sklearn on real datasets.

Real-World Applications

  • Customer Segmentation: Group customers for targeted marketing.
  • Healthcare Clusters: Identify patient groups for tailored care.
  • Financial Analysis: Cluster by risk for smarter credit scoring.

Skills You’ll Develop

  • Data Pre-Processing
  • Choosing Clustering Techniques
  • Algorithm Proficiency: Practice with k-means, DBSCAN, and more.

Tools & Technologies

  • Programming: Python & SQL
  • Visualization: Tableau, Power BI
  • Libraries: pandas, matplotlib

Why upGrad?

  • Global Partnerships: Learn with IIIT-B and more.
  • Industry Projects: Real-world case studies for job-ready skills.
  • Flexible Learning: Online with 24/7 mentor support.

Start learning with upGrad today!

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Frequently Asked Questions (FAQs)

1. How is clustering different from classification in data mining?

Clustering v/s classification - Clustering groups data points based on similarities without using labels, while classification assigns known labels to data based on examples. Clustering is about finding patterns, while classification is about sorting data into predefined categories.

2. Does clustering need a specific dataset size to work well?

Clustering can work with various dataset sizes. Larger datasets often reveal clearer patterns, but very large datasets might need more processing power.

3. How do I choose the right number of clusters?

There are methods like the Elbow Method or Silhouette Score that help decide the best number by showing where clusters are well-separated.

4. Can clustering handle noisy data or outliers?

Some methods, like DBSCAN, handle noise and outliers better by focusing on dense clusters and ignoring sparse, scattered data points.

5. Can I use clustering with both numerical and categorical data?

Yes, specific clustering methods, like k-modes, work with categorical data, while numerical data can use methods like K-Means.

6. What are some common challenges in clustering?

Some common challenges include choosing the right method, deciding on the number of clusters, handling high-dimensional data, and managing noise or outliers.

7. Which clustering method works best with high-dimensional data?

For high-dimensional data, methods like Spectral Clustering or combining PCA with K-Means often work well.

8. Can clustering work with real-time data?

Yes, some clustering algorithms are designed for streaming data and can update clusters as new data comes in.

9. How does reducing dimensions affect clustering?

Reducing dimensions helps simplify data, often making clustering faster and more effective by focusing only on important features.

10. Can clustering be combined with other data mining techniques?

Yes, clustering is often combined with classification or association analysis to give more complete insights into data patterns.

11. What is cluster labeling, and why is it useful?

Cluster labeling gives meaningful names to clusters based on their traits which makes it easier to understand and use clustering results.



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But there were three problems with this – the interviews weren’t coordinated well enough to get a holistic view of the candidate, we were never really sure if their answers would translate to effective performance on the job, and from the perspective of the candidate it was a pretty lengthy interrogation. So, a new interview process need to be designed that is much more effective and transparent – we want to give the candidate a sense for what a day in the life of a member on the team is like, and get a read on what it would be like to work with a company. In total it takes about two days to make a decision, and there be no false positives (possibly some false negatives though), and the feedback from both the candidates and the team members has been positive. There are four steps to the process: Resume/phone screens – look for people who have experience using data to drive decisions, and some knowledge of what your company is all about. On both counts you’ll get a much deeper read later in the process; you just want to make sure that moving forward is a good use of either of both of your time. Basic data challenge – The goal here is to validate the candidate’s ability to work with data, as described in their resume. So send a few data sets to them and ask a basic question; the exercise should be easy for anyone who has experience. In-house data challenge – This is should be the meat of the interview process. Try to be as transparent about it as possible – they’ll get to see what it’s like working with you and vice versa. So have the candidate sit with the team, give them access to your data, and a broad question. They then have the day to attack the problem however they’re inclined, with the support of the people around them. Do encourage questions, have lunch with them to ease the tension, and check-in periodically to make sure they aren’t stuck on something trivial. At the end of the day, we gather a small team together and have them present their methodology and findings to you. Here, look for things like an eye for detail (did they investigate the data they’re relying upon for analysis), rigor (did they build a model and if so, are the results sound), action-oriented (what would we do with what you found), and communication skills. Read between the resume lines Intellectual curiosity is what you should discover from the project plans. It’s what gives the candidate the ability to find loopholes or outliers in data that helps crack the code to find the answers to issues like how a fraudster taps into your system or what consumer shopping behaviors should be considered when creating a new product marketing strategy. Data scientists find the opportunities that you didn’t even know were in the realm of existence for your company. They also find the needle in the haystack that is causing a kink in your business – but on an entirely monumental scale. In many instances, these are very complex algorithms and very technical findings. However, a data scientist is only as good as the person he must relay his findings to. Others within the business need to be able to understand this information and apply these insights appropriately. Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses Good data scientists can make analogies and metaphors to explain the data but not every concept can be boiled down in layman’s terms. A space rocket is not an automobile and, in the brave new world, everyone must make this paradigm shift. Top Data Science Skills You Should Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Online Certification Inferential Statistics Online Certification 2 Hypothesis Testing Online Certification Logistic Regression Online Certification 3 Linear Regression Certification Linear Algebra for Analysis Online Certification upGrad’s Exclusive Data Science Webinar for you – Watch our Webinar on The Future of Consumer Data in an Open Data Economy document.createElement('video'); https://cdn.upgrad.com/blog/sashi-edupuganti.mp4 Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Our learners also read: Free Python Course with Certification And lastly, the data scientist you’re looking for needs to have strong business acumen. Do they know your business? Do they know what problems you’re trying to solve? And do they find opportunities that you never would have guessed or spotted?
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by upGrad

14 May'16
UpGrad partners with Analytics Vidhya

5.69K+

UpGrad partners with Analytics Vidhya

We are happy to announce our partnership with Analytics Vidhya, a pioneer in the Data Science community. Analytics Vidhya is well known for its impressive knowledge base, be it the hackathons they organize or tools and frameworks that they help demystify. In their own words, “Analytics Vidhya is a passionate community for Analytics/Data Science professionals, and aims at bringing together influencers and learners to augment knowledge”. Explore our Popular Data Science Degrees Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Degrees We are joining hands to provide candidates of our PG Diploma in Data Analytics, an added exposure to UpGrad Industry Projects. While the program already covers multiple case studies and projects in the core curriculum, these projects with Analytics Vidhya will be optional for students to help them further hone their skills on data-driven problem-solving techniques. To further facilitate the learning, Analytics Vidhya will also be providing mentoring sessions to help our students with the approach to these projects. Our learners also read: Free Online Python Course for Beginners Top Essential Data Science Skills to Learn SL. No Top Data Science Skills to Learn 1 Data Analysis Certifications Inferential Statistics Certifications 2 Hypothesis Testing Certifications Logistic Regression Certifications 3 Linear Regression Certifications Linear Algebra for Analysis Certifications This collaboration brings great value to the program by allowing our students to add another dimension to their resume which goes beyond the capstone projects and case studies that are already a part of the program. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? Through this, we hope our students would be equipped to showcase their ability to dissect any problem statement and interpret what the model results mean for business decision making. This also helps us to differentiate UpGrad-IIITB students in the eyes of the recruiters. upGrad’s Exclusive Data Science Webinar for you – Transformation & Opportunities in Analytics & Insights document.createElement('video'); https://cdn.upgrad.com/blog/jai-kapoor.mp4 Check out our data science training to upskill yourself
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by Omkar Pradhan

09 Oct'16
Data Analytics Student Speak: Story of Thulasiram

5.69K+

Data Analytics Student Speak: Story of Thulasiram

When Thulasiram enrolled in the UpGrad Data Analytics program, in its first cohort, he was not very different for us, from the rest of our students in this. While we still do not and should not treat learners differently, being in the business of education – we definitely see this particular student in a different light. His sheer resilience and passion for learning shaped his success story at UpGrad. Humble beginnings Born in the small town of Chittoor, Andhra Pradesh, Thulasiram does not remember much of his childhood given that he enlisted in the Navy at a very young age of about 15 years. Right out of 10th standard, he trained for four years, acquiring a diploma in mechanical engineering. Thulasiram came from humble means. His father was the manager of a small general store and his mother a housewife. It’s difficult to dream big when leading a sheltered life with not many avenues for exposure to unconventional and exciting opportunities. But you can’t take learning out of the learner. “One thing I remember about school is our Math teacher,” reminisces Thulasiram, “He used to give us lot of puzzles to solve. I still remember one puzzle. If you take a chessboard and assume that all pawns are queens; you have to arrange them in such a way that none of the eight pawns should die. Every queen, should not affect another queen. It was a challenging task, but ultimately we did it, we solved it.” Navy & MBA At 35 years of age, Thulasiram has been in the navy for 19 years. Presently, he is an instructor at the Naval Institute of Aeronautical Technology. “I am from the navy and a lot of people don’t know that there is an aviation wing too. So, it’s like a dream; when you are a small child, you never dream of touching an aircraft, let alone maintaining it. I am very proud of doing this,” says Thulasiram on taking the initiative to upskill himself and becoming a naval-aeronautics instructor. When the system doesn’t push you, you have to take the initiative yourself. Thulasiram imbibed this attitude. He went on to enroll in an MBA program and believes that the program drastically helped improve his communication skills and plan his work better. How Can You Transition to Data Analytics? Data Analytics Like most of us, Thulasiram began hearing about the hugely popular and rapidly growing domain of data analytics all around him. Already equipped with the DNA of an avid learner and keen to pick up yet another skill, Thulasiram began researching the subject. He soon realised that this was going to be a task more rigorous and challenging than any he had faced so far. It seemed you had to be a computer God, equipped with analytical, mathematical, statistical and programming skills as prerequisites – a list that could deter even the most motivated individuals. This is where Thulsiram’s determination set him apart from most others. Despite his friends, colleagues and others that he ran the idea by, expressing apprehension and deterring him from undertaking such a program purely with his interests in mind – time was taken, difficulty level, etc. – Thulasiram, true to the spirit, decided to pursue it anyway. Referring to the crucial moment when he made the decision, he says, If it is easy, everybody will do it. So, there is no fun in doing something which everybody can do. I thought, let’s go for it. Let me push myself — challenge myself. Maybe, it will be a good challenge. Let’s go ahead and see whether I will be able to do it or not. UpGrad Having made up his mind, Thulasiram got straight down to work. After some online research, he decided that UpGrad’s Data Analytics program, offered in collaboration with IIIT-Bangalore that awarded a PG Diploma on successful completion, was the way to go. The experience, he says, has been nothing short of phenomenal. It is thrilling to pick up complex concepts like machine learning, programming, or statistics within a matter of three to four months – a feat he deems nearly impossible had the source or provider been one other than UpGrad. Our learners also read: Top Python Free Courses Favorite Elements Ask him what are the top two attractions for him in this program and, surprising us, he says deadlines! Deadlines and assignments. He feels that deadlines add the right amount of pressure he needs to push himself forward and manage time well. As far as assignments are concerned, Thulasiram’s views resonate with our own – that real-life case studies and application-based learning goes a long way. Working on such cases and seeing results is far superior to only theoretical learning. He adds, “flexibility is required because mostly only working professionals will be opting for this course. You can’t say that today you are free, because tomorrow some project may be landing in your hands. So, if there is no flexibility, it will be very difficult. With flexibility, we can plan things and maybe accordingly adjust work and family and studies,” giving the UpGrad mode of learning, yet another thumbs-up. Amongst many other great things he had to say, Thulasiram was surprised at the number of live sessions conducted with industry professionals/mentors every week. Along with the rest of his class, he particularly liked the one conducted by Mr. Anand from Gramener. Top Data Science Skills to Learn to upskill SL. No Top Data Science Skills to Learn 1 Data Analysis Online Courses Inferential Statistics Online Courses 2 Hypothesis Testing Online Courses Logistic Regression Online Courses 3 Linear Regression Courses Linear Algebra for Analysis Online Courses What Kind of Salaries do Data Scientists and Analysts Demand? Get data science certification from the World’s top Universities. Learn Executive PG Programs, Advanced Certificate Programs, or Masters Programs to fast-track your career. Read our popular Data Science Articles Data Science Career Path: A Comprehensive Career Guide Data Science Career Growth: The Future of Work is here Why is Data Science Important? 8 Ways Data Science Brings Value to the Business Relevance of Data Science for Managers The Ultimate Data Science Cheat Sheet Every Data Scientists Should Have Top 6 Reasons Why You Should Become a Data Scientist A Day in the Life of Data Scientist: What do they do? Myth Busted: Data Science doesn’t need Coding Business Intelligence vs Data Science: What are the differences? upGrad’s Exclusive Data Science Webinar for you – ODE Thought Leadership Presentation document.createElement('video'); https://cdn.upgrad.com/blog/ppt-by-ode-infinity.mp4 Explore our Popular Data Science Courses Executive Post Graduate Programme in Data Science from IIITB Professional Certificate Program in Data Science for Business Decision Making Master of Science in Data Science from University of Arizona Advanced Certificate Programme in Data Science from IIITB Professional Certificate Program in Data Science and Business Analytics from University of Maryland Data Science Courses “Have learned most here, only want to learn..” Interested only in learning, Thulasiram made this observation about the program – compared to his MBA or any other stage of life. He signs off calling it a game-changer and giving a strong recommendation to UpGrad’s Data Analytics program. We are truly grateful to Thulasiram and our entire student community who give us the zeal to move forward every day, with testimonials like these, and make the learning experience more authentic, engaging, and truly rewarding for each one of them. If you are curious to learn about data analytics, data science, check out IIIT-B & upGrad’s PG Diploma in Data Science which is created for working professionals and offers 10+ case studies & projects, practical hands-on workshops, mentorship with industry experts, 1-on-1 with industry mentors, 400+ hours of learning and job assistance with top firms.
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by Apoorva Shankar

07 Dec'16