Best Advanced Excel Class in Kolkata, India

Top Options for Advanced Excel in Kolkata

 

Good question. Here are some of the best places to learn Advanced Excel in Kolkata, plus a few points to help you choose depending on your goals:

1. Palium Skills

   • They offer an Advanced Excel course in Kolkata. (Palium Skills)

   • Topics: Pivot Tables, charts, advanced formulas, macros, Index/Match, etc. (Palium Skills)

   • Mode: Classroom (weekdays/weekends) + Live online batches. (Palium Skills)

   • Fee: ~₹4,000+ (classroom) for their regular batch. (Palium Skills)

2. Aptech – Lake Town, Kolkata

   • Aptech Lake Town has a dedicated Advanced Excel Course for business and data analysis use. (aptechlaketown.in)

   • Duration: ~2 months. (aptechlaketown.in)

   • Covers: Advanced formulas, PivotTables, VBA/macros, automation etc. (aptechlaketown.in)

   • Certification: Yes, they give a certificate upon completion. (aptechlaketown.in)

3. ACTE (Kolkata branch)

   • Offers a 40-hour Advanced Excel training. (ACTE Technologies)

   • Focus: Macros, conditional formatting, What-If analysis, functions, and more. (ACTE Technologies)

   • Job-oriented: They mention it's very relevant for roles needing data handling / MIS. (ACTE Technologies)

4. Ascent Infotech Computer Training Institute

   • Their Advanced Excel course includes “data analysis and automation” via macros. (ascentcomputer.co.in)

   • Practical, hands-on training. (ascentcomputer.co.in)

   • Certification from the institute after completion. (ascentcomputer.co.in)

5. NIOSDiT

   • Certificate in Advanced Excel (CAE) course. (NiOSDiT)

   • Duration: 3 months, 2 classes/week. (NiOSDiT)

   • Cost: ₹5,000 for the course. (NiOSDiT)

   • Mode: Offline (classroom training). (NiOSDiT)

6. Bhairab Ganguly College

   • They have an “Advanced Excel” add-on course. (bhairabgangulycollege.ac.in)

   • Covers: Pivot tables, macros (VBA), data analysis, error checking, scenario analysis. (bhairabgangulycollege.ac.in)

7. Women’s Christian College, Kolkata

   • Certificate course on Advanced Excel (for students) through their Computer Training Centre. (womenschristiancollege.net)

   • Good for students who want to build Excel skills for academics or future careers.

8. Sprintzeal

   • Microsoft Excel certification training, covering basic to advanced topics. (Sprintzeal.com)

   • Flexible mode: In-person or live online. (Sprintzeal.com)

   • Includes preparation for data analysis, PivotTables, advanced formulae, etc. (Sprintzeal.com)

My Recommendation

• For Professionals / Data Analysts → Palium Skills or Aptech Lake Town: Both have very practical, industry-relevant advanced Excel content.

• For Certification + Long-Term Use → ACTE: Good mix of theory + practice + certification.

• For Students / Learners on a Budget → NIOSDiT or Women’s Christian College: Affordable and credible.

• For Academic / Data Projects → Bhairab Ganguly College: Useful for advanced data manipulation and scenario analysis.

As Excel is widely used across all domain and industries. It is one of the most popular courses amongst professionals. There are many places big and small that offer courses in Excel. However, one needs to learn it properly to get full value from a course. So it is important to learn it from the right place.

Here’s a summary of current (2025) batch schedules + fees for some top Advanced Excel courses in Kolkata, India:

📊 Current (2025) Advanced Excel Course Details — Kolkata

Institute	Format / Mode	Batch Schedule	Fees
Palium Skills	Classroom or Live Online	- Offline Weekday batch starts 1 Dec 2025 (Palium Skills) - Online/Offline Weekend batch starts 7 Dec 2025 (Palium Skills)	- Classroom: ₹5,000         - One-to-One: ₹10,500     - Online Live: ₹5,000
ACTE, Kolkata	Classroom / Online	- Weekday Regular batch: 27 Oct 2025 (ACTE Technologies) - Weekend Regular batch: 1 Nov 2025 (ACTE Technologies) - Weekend Fast-track: 2 Nov 2025 (ACTE Technologies)	₹10,000 for 40+ hours course (ACTE Technologies)
Aptech (Lake Town)	Classroom / Flexible Schedule	Duration: ~2 months for the Advanced Excel course. (aptechlaketown.in)	₹6,000 for 20 hours course  call them to ask for the exact fee for 2025.

✅ My Thoughts

• Best for Budget + Practical Training: Palium Skills is quite affordable and has flexible batch modes with excellent faculty. It has multiple offerings that can be used.

• Best for Certification & Depth: ACTE’s 40-hour program is intensive, giving a solid foundation + hands-on work.

• Good for In-Person Learning: Aptech is ideal if you prefer face-to-face classes and a longer course duration.

If you like, I can check all Advanced Excel courses in Kolkata (including smaller institutes) running in the next 2-3 months, so you can pick the one that fits your timings and budget. Do you want me to do that?

Use cases describing use of Machine Learning

Use Cases describing use of Machine Learning

Machine learning (ML) is a field of artificial intelligence (AI) that allows computers to learn from data and make predictions or decisions without being explicitly programmed. It has many applications across industries, including healthcare, finance, retail, entertainment, and more. Here are some general use cases of machine learning:

1. Predictive Analytics: ML can predict future events based on historical data. In business, this could mean forecasting sales, customer behavior, or stock prices.
2. Natural Language Processing (NLP): This includes applications like chatbots, sentiment analysis, and machine translation (like Google Translate).
3. Computer Vision: ML is used for image recognition and classification, such as in facial recognition, medical image analysis, or autonomous vehicles.
4. Recommendation Systems: Platforms like Netflix, Amazon, and YouTube use machine learning to recommend movies, products, or videos based on user behavior.
5. Anomaly Detection: ML algorithms can identify unusual patterns in data, which is useful for fraud detection in banking, network security, or health monitoring.
6. Personal Assistants: ML powers virtual assistants like Siri, Alexa, and Google Assistant, which use speech recognition and NLP to understand and respond to user queries.

One fascinating specific example of how machine learning can be applied is in analyzing historical data, such as the passengers of the Titanic.

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 Example Use Case: Predicting Titanic Survivors Using Machine Learning

A well-known example in the machine learning community is predicting the survival of passengers on the Titanic. The Titanic dataset contains details about the passengers aboard the ill-fated ship, such as their age, sex, class, and whether they survived or not. This data is often used as a beginner's project to demonstrate classification algorithms in ML.

 Dataset Overview:

The Titanic dataset consists of the following columns (features):
- PassengerId: Unique ID of the passenger.
- Pclass: The class of the passenger (1st, 2nd, or 3rd class).
- Name: The name of the passenger.
- Sex: The gender of the passenger (male or female).
- Age: The age of the passenger.
- SibSp: The number of siblings or spouses aboard the Titanic.
- Parch: The number of parents or children aboard.
- Ticket: The ticket number.
- Fare: The fare the passenger paid for the ticket.
- Cabin: The cabin where the passenger stayed (often missing).
- Embarked: The port at which the passenger boarded (C = Cherbourg, Q = Queenstown, S = Southampton).
- Survived: The target variable (1 = survived, 0 = did not survive).

 Objective:
The goal is to predict whether a passenger survived or not based on these features. This is a binary classification problem where the outcome (survived or not) is binary (0 or 1).

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 Step-by-Step Example: Titanic Survival Prediction Using ML

 Step 1: Data Preprocessing

- Handle Missing Data: Some features, such as Age, Cabin, and Embarked, might have missing values. You would typically fill missing values with the median (for numerical data) or the most frequent value (for categorical data) or remove rows with too many missing values.
- Feature Engineering: Create new features that could be useful, such as:
  - Family Size: Combine "SibSp" and "Parch" to get the total family size aboard.
  - Title: Extract titles from the Name field (Mr., Mrs., etc.) to understand social status or age group.
  - Age Group: Convert age into categories (e.g., child, adult, elderly) if this is more predictive.

 Step 2: Feature Selection

- Select the most important features for training. For example, gender (Sex) is often a crucial feature in predicting survival, as women were more likely to survive. Pclass, Age, and Fare can also be important features.

 Step 3: Model Selection

- Choose an Algorithm: You could use a variety of ML models for this task, such as:
  - Logistic Regression: A simple model for binary classification.
  - Decision Trees: A tree-like model that splits data based on the most important features.
  - Random Forests: An ensemble of decision trees to reduce overfitting.
  - Support Vector Machines (SVM): A powerful classifier that works well for high-dimensional data.
  - Neural Networks: A more complex model, though often overkill for smaller datasets like this.

 Step 4: Model Training

- Train the model on a portion of the data (training set), and validate it using a separate part of the data (test set). You could use techniques like cross-validation to avoid overfitting and get a better estimate of how the model will perform on unseen data.

 Step 5: Model Evaluation

- Evaluate the performance of the model using metrics such as:
  - Accuracy: The percentage of correct predictions.
  - Precision: The proportion of true positives (survived passengers) among all positive predictions.
  - Recall: The proportion of true positives among all actual positives.
  - F1 Score: The harmonic mean of precision and recall, useful when the dataset is imbalanced.

 Step 6: Model Tuning

- Fine-tune the model's hyperparameters (e.g., regularization strength, depth of trees, etc.) to improve performance. This can be done using grid search or random search for hyperparameter optimization.

 Step 7: Predictions

- Once the model is trained and evaluated, you can use it to make predictions about new passengers' survival (e.g., people who were not on the Titanic but have similar characteristics).

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Example: Key Insights from Titanic Prediction

After running the machine learning model on the Titanic dataset, you might find several insights that are both informative and actionable, such as:

1. Gender is the most important factor: The model might show that women had a significantly higher chance of survival than men. This aligns with historical records where women and children were prioritized during the evacuation.
2. Pclass matters: Passengers in higher classes (1st class) had a much better chance of survival than those in 3rd class, likely due to the location of their cabins and their proximity to the lifeboats.
3. Age and Family Size: Children and passengers traveling with families might have had higher survival rates, as they were often prioritized for lifeboats.
4. Fare: Wealthier passengers (who paid higher fares) were more likely to survive, again reflecting the social inequalities of the time.

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 Potential Impact of ML in This Case

Machine learning models can help researchers, historians, or analysts extract patterns from historical datasets that were previously hard to quantify. In the Titanic example, using machine learning can reveal biases and social factors (such as class and gender) that influenced survival chances in ways that could be overlooked in manual analysis.

Moreover, ML can also be extended to more complex datasets, such as modern disaster survival analysis, helping authorities and organizations optimize evacuation procedures or make better-informed decisions during critical situations.

Palium Skills conducts courses on Artificial Intelligence, Machine Learning and Python Programming with hands-on example and learning.

 Conclusion

The Titanic survival prediction is a classic example of how machine learning can be used for classification problems. It demonstrates the power of algorithms to learn from historical data, uncover patterns, and make predictions about future or unseen data. This kind of analysis is valuable not only in historical contexts but can also be applied to current real-world situations such as disaster management, insurance, and even personalized recommendations.