16 Weeks Data Analytics Work Placement

Why Choose Us?

What You will Get:

Practical, hands-on experience working within an IT company, collaborating with teams of engineers while practicing Agile methodologies and Scrum to deliver real projects.

Essential soft skills development, a key factor that helps bridge the gap for migrants entering the workforce.

Professional CV and LinkedIn profile reviews, making you stand out to potential employers.

Mock job interviews led by a former lead talent recruiter from a top global IT firm to help you ace your next interview.

A work reference and dedicated job search support to help you find a suitable role during and after your placement.

Task: Dataset of sales transactions over the past year are provided. You are required to clean and preprocess the data, identifying trends, seasonality, and any anomalies. You should then build a predictive model to forecast future sales based on historical data.
Skills Gained: Data cleaning, feature engineering, time series analysis, and building predictive models.
Tools:
- Data Cleaning and Preprocessing: Python (Pandas, NumPy), Excel, or R
- Data Visualization: Matplotlib, Seaborn (Python), Power BI, or Tableau
- Forecasting Model: ARIMA (Python, statsmodels), Prophet (Python), or any machine learning regression models (e.g., Random Forest or XGBoost in Python)
- Automation: Jupyter Notebooks or Google Colab

Task: You are provided a customer dataset that includes demographic and transaction information. Your task is to analyze customer behavior and segment customers into distinct
groups based on purchasing patterns or other metrics (e.g., K-means clustering or hierarchical clustering).
Skills Gained: Data preprocessing, clustering algorithms, market segmentation analysis and visualization
Tools:
- Data Cleaning and Preprocessing: Python (Pandas, NumPy), Excel, or R
- Clustering Algorithms: Scikit-learn (Python) for K-means,
  DBSCAN, or hierarchical clustering
- Visualization: Matplotlib, Seaborn (Python), Tableau, or Power BI (to visualize clusters and insights)
- Reporting: Jupyter Notebooks or Google Colab for analysis and documentation.

Task: Social media data (such as tweets or Facebook posts) about a specific topic or brand are provide. You are required to use natural language processing (NLP) techniques to determine the overall sentiment (positive, negative, or neutral) and uncover trends in public perception.
Skills Gained:
Text mining, sentiment analysis using NLP libraries (e.g., NLTK, TextBlob, or Spacy), and
visualization of results with Python or business intelligence tools.
Tools:
- Data Collection:Tweepy (Python) for accessing Twitter API, or other APIs for social media data
- Text Preprocessing:NLTK, SpaCy, or TextBlob (Python)
  Sentiment Analysis:TextBlob, VADER (Python), or Hugging Face Transformers for more advanced models
- Visualization:Matplotlib, Seaborn (Python), Power BI, or Tableau (to display sentiment trends over time)
- Reporting:Jupyter Notebooks or Google Colab for analysis and insights.

Task: Use the provided financial dataset with transactions and account activities. You are required to identify unusual patterns in the data that might indicate fraud, like large transactions from new accounts or multiple transactions within a short period.
Skills Gained: Anomaly detection, data transformation,
and understanding of financial transactions. Students can use machine learning algorithms to detect anomalies, such as decision trees or neural networks, and apply tools like Python (Scikit-learn, TensorFlow) for model development.
Tools:
- Data Cleaning:Python (Pandas, NumPy), Excel, or R
- Anomaly Detection Algorithms:Scikit-learn (Python) for Isolation Forest, One-Class SVM, or Random Cut Forest; Autoencoders (Keras/TensorFlow for neural networks)
- Visualization:Matplotlib, Seaborn (Python), Tableau, or Power BI (to visualize fraudulent transactions) Model
- Evaluation:Python (Scikit-learn, confusion matrix, ROC curve)
- Reporting:Jupyter Notebooks or Google Colab for presenting results