Sherlock & I
An introductory course on the principles of Data Analytics, Data Science and the Python programming language, viewed through the lens of the world’s greatest consulting detective - Sherlock Holmes. Learn to observe, deduce, and present findings, transforming raw clues (data) into compelling solutions.
“Data! Data! Data! I can’t make bricks without clay.” - Sherlock Holmes (from The Adventure of the Copper Beeches)
Investigate ↓
Instructor-led.
In Sherlock & I, you’re never solving mysteries alone - this is a fully instructor-led adventure. Whether you’re seated in the classroom at 221B Baker Street (okay, not literally) or joining from your digital study in London, Delhi, or Dindigul, you’ll be guided live by a brilliant instructor who plays the part of Holmes with code instead of a violin.
Ask questions, follow clues, and get real-time feedback as you crack cases together. Every mystery is more thrilling when Holmes is just a question away!
Small Class Size.
At Sherlock & I, we believe every detective deserves personal attention - after all, Holmes never solved mysteries in a crowd! That’s why we keep our classes intentionally small, with a maximum of 25 students per batch. This ensures that every Watson-in-training gets plenty of time to ask questions, share theories, and get one-on-one guidance from the instructor.
Fewer suspects, clearer clues, and more meaningful “Aha!” moments for everyone. It’s elementary!
Learn Data Analytics in 8 weeks.
Sherlock & I is a 8-week immersive investigation into the world of data, deduction, and dazzling Python skills. In just a month and a half, you’ll go from curious coder to consulting data detective-cracking digital mysteries week by week.
Each session builds on the last like chapters in a gripping Holmes novel, and by the final page, you’ll be solving capstone cases worthy of Scotland Yard. Eight weeks, one unforgettable adventure!
HYBRID CLASSES
Onsite & Online
Code with us from the classroom or your home. Our hybrid classes let you join the fun from anywhere on Earth (or beyond, if your Wi-Fi’s good enough).
ONSITE
If you’re in Bangalore, grab your magnifying glass and join your fellow detectives at Ace Hacker HQ, J.P. Nagar 7th Phase - our very own 221B Baker Street! But if you’re sleuthing from elsewhere on the planet, no problem at all - just beam into the investigation online, live and in sync with the classroom.
Whether you’re in Bengaluru or Buenos Aires, the mystery unfolds for everyone, together.
ONLINE
Whether you’re dialing in from Tokyo, decoding clues in Timbuktu, or just too cozy to cross Bangalore traffic - join the case online from anywhere.
The adventures of Sherlock & I don’t care about your pin code - all you need is a screen, an internet connection, and your best Watson-worthy curiosity. World-class mystery-solving fun, delivered straight to your lair.
About this Course
In Sherlock & I, you don’t just write code - you investigate it. As Watson to Sherlock Holmes, you’ll tackle intricate coding mysteries, unravel hidden bugs, and deduce the secrets of writing elegant, efficient, and maintainable code. Each lesson unfolds like a gripping detective story, where every misplaced semicolon or inefficient algorithm is a clue waiting to be uncovered. If you've ever wanted to refine your coding instincts, think like a master problem-solver, and turn debugging into an art form, this course is your magnifying glass. Sign up, and let’s crack the case - one line of code at a time.
“It is a capital mistake to theorize before one has data.” We begin by understanding why we must first gather facts before drawing conclusions. Every investigation begins with the basics.
- What is Data Analytics? The process of inspecting, cleaning, transforming, and modeling data.
- The Data Analyst’s Mindset: Observation vs. Deduction.
- Introduction to the Python toolkit: Setting up your environment (Jupyter Notebook/Google Colab).
- Basic Python Syntax: Variables, data types (strings, integers, floats), and comments.
- Lab: “Tea with Holmes” - Load a CSV dataset about Holmes’ daily case notes. Use basic Python and pandas to explore the data, understand columns, view summary stats, and print your first data insights.
From a battered hat, Holmes deduces the owner’s age, intellect, and lifestyle. We learn to extract initial insights from a dataset.
- Exploratory Data Analysis (EDA)
- The goal of EDA: Summarizing the main characteristics of a dataset
- Understanding dataset dimensions, data types, and initial statistics
- Pandas Library: Introduction to DataFrames
- Loading data:
pd.read_csv() - First look at data:
.head(),.tail(),.info(),.describe(),.shape
Holmes uses frequency analysis to decode a secret cipher based on letter usage in English. This module teaches students how frequency counts and bar plots can help uncover hidden patterns in datasets.
- Understanding frequency distributions
- Analyzing categorical data to find patterns
pandas.value_counts()matplotlib.pyplot.bar()collections.Counter- Lab: “The Dancing Code” - Given an encrypted message of dancing symbols (mapped to letters), perform frequency analysis to guess letters and decode the message. Use Counter and bar plots to visualize symbol frequencies.
The most important clue was what “didn’t” happen. The absence of a bark was an anomaly that pointed to the culprit. Here, we learn to use statistical methods to detect outliers, and learn to interpret missing or null data - the silent clues that often solve the case.
- What is an outlier or an anomaly?
- Using statistics to identify data points that deviate from the norm
- boolean Filtering
isnull()dropna()fillna()- Lab: “The dog didn’t bark” - Analyze a dataset of guard dog reports and stable logs. Identify missing values (e.g., missing logs for the dog), interpret their significance, and clean the data accordingly.
Fear and myth obscure the truth in this story. Learn how to analyze text sentiment and extract facts from fiction using NLP - just like Holmes separates superstition from logic.
nltkTextBlobCounter- Word clouds
- Basic sentiment scoring
- Lab: “Tales from the Moor” - Given text-based eyewitness accounts, perform sentiment analysis using
TextBlob. Classify statements as factual or emotional, and visualize word clouds to identify recurring themes.
- The dataset: You will be given a rich dataset containing:
- A timeline of mysterious events on the moor.
- The Baskerville family history (genealogy, causes of death).
- Witness statements and character profiles.
- Local legends and their attributes.
- Your mission:
- Clean and Prepare the disparate sources of information.
- Perform EDA to understand the characters, locations, and timelines.
- Analyze frequencies of events and identify anomalies in witness reports.
- Visualize the relationships and timeline to build your case.
- Present your final deduction: A report that identifies the culprit and explains how the data supports your conclusion.
Holmes discovers the hidden photograph by interpreting visual clues and subtle cues. This module teaches students how to load, inspect, and even analyze image files using metadata.
Pillowmatplotlib.pyplot.imshow()os- Image properties
- Lab: “The Photo Clue” - Work with a set of image files (metadata only). Extract resolution, file size, creation date, etc. Use Python to identify the original image based on clues from Holmes’ notes.
Holmes spots an unusual pattern (a vent, a rope, and a whistle). Students learn to identify outliers in data that don’t follow the expected pattern.
describe()- box plots
- z-score
- IQR method
- Lab: “The Unusual Room” - Analyze a home layout dataset with sensor data from rooms. Use box plots and z-scores to detect anomalies (like strange movements in the middle of the night).
Holmes solves an ancient riddle involving distances and directions. Students solve treasure hunt puzzles using graphs and coordinate-based analysis.
networkx- Pathfinding (e.g., Dijkstra’s algorithm)
- Adjacency lists
- Lab: “The Algorithmic Treasure Hunt” - Use coordinate data and directional clues from a riddle to simulate a treasure hunt. Construct a simple graph and find the shortest path to the treasure.
Holmes backtracks a stolen gem’s journey through layers of ownership. Similarly, students learn to filter and trace records through chains of conditions. Also, from a battered hat, Holmes deduces the owner’s age, intellect, and lifestyle. We learn to extract initial insights from a dataset.
pandasfilters.loc[].query()- Condition chaining
- Lab: “The Goose Trail” - Trace a valuable object through multiple owners using a transaction log dataset. Use filtering, sorting, and conditional logic to reconstruct the timeline and catch the thief.
Stolen documents are hidden using secret codes. Students learn simple encryption techniques and frequency-based decryption.
- Caesar cipher in Python
- Basic modular arithmetic
- String manipulation
- Lab: “Secrets of the Cipher” - Implement Caesar and simple substitution ciphers. Given an encoded message in a dataset, apply decryption using frequency-based analysis and string operations.
Holmes predicts a murder sequence using the pattern of orange pips. Students learn to train linear regression models to make data-driven predictions.
scikit-learn- Linear Regression
train_test_splitmean_squared_error- Lab: “Letters of Doom” - Use a dataset of past victims who received orange pips. Train a linear regression model to predict time of attack based on number of days after receiving the pips.
A seemingly good job offer turns into a trap. Students learn how to classify outcomes - good vs bad, spam vs. not spam-based on data features.
- K-Nearest Neighbors (KNN)
- Decision Trees
- Confusion Matrix
- Accuracy Scoring
- Lab: “Offer too good to be true” - Use classification (KNN/Decision Tree) to detect fraudulent job offers from real ones based on features like salary, work hours, recruiter source, etc.
Holmes disappears, and clues are revealed over time. Students explore trends and changes in data across time intervals.
pandas.to_datetime()- Indexing by time
- Rolling averages
matplotlibLine plots- Lab: “The fall and the timeline” - Analyze a dataset of Holmes and Moriarty sightings leading up to the fall. Use time-series plots to detect movement patterns and predict confrontation time.
Holmes sees connections others miss. This module shows how variables interact and how to measure those relationships
corr()- Scatter plots
- Heatmaps
seaborn- Lab: “Connections only Holmes could see” - Use a multivariate dataset (e.g., crime data across cities). Calculate correlations, draw scatter plots, and generate heatmaps to find relationships.
Just as Holmes pieces together a puzzle with missing parts, students learn to clean, impute, and prepare incomplete datasets.
- Imputation strategies
- String cleaning
dropna()- Regex
- Lab: “The case of the disappearing values” - lean a messy dataset with random missing values. Apply imputation techniques (mean, median, or custom logic) and prepare the data for modeling.
Holmes gathers hidden data from different sources; students learn to extract structured data from websites to build their own datasets.
BeautifulSouprequests- HTML parsing
pandas.DataFrame()from scraped tables- Lab: “Holmes goes online” - Scrape a fictional criminal report archive (mock HTML or real site like IMDb crime stories). Extract key data points and convert them into a usable dataset.
A respectable gentleman is disguised as a beggar. Holmes cleans away the grime to reveal the truth. We learn to clean "dirty" data.
- Identifying and handling missing values (nulls)
- Correcting incorrect data types
- Filtering and selecting relevant data
.isnull().sum().fillna().dropna()
This is the learner’s showdown with “Professor Moriarty” - a complex dataset filled with clues and red herrings. Students apply all techniques learned to solve the mystery and present findings.
- Data import
- EDA
- Cleaning
- Modeling
- Visualization
- Storytelling
- Capstone Lab: “The Moriarty Dataset” - You receive a massive dataset of Moriarty’s criminal network. Your mission: clean the data, explore patterns, detect key collaborators, visualize findings, and present a final report (or Jupyter Notebook dashboard) to Scotland Yard.
Learn Data Analytics & Data Science.
Choose a schedule that works best for you. Join from anywhere across the globe. Ace Hacker is a Hybrid / Hyflex Classroom learning environment. The classroom includes a mix of students who are present onsite and those who wish to join the class virtually.
Weekdays
Mon, Tue, Wed, Thu
Duration: 8 weeks
4:00 PM - 5:30 PM
India Time
(1 hr 30 mins per day)
/
Class Format:
Hybrid Classes
Fee, excluding tax (in INR)
₹ 42,000
+ GST: ₹7,560
Convert to USD
Next batch begins from:
In Progress...
Seat Availability
(Classroom full)
Weekends
Saturday & Sunday
Duration: 8 weeks
12:30 PM - 3:30 PM
India Time
(3 hours per day)
/
Class Format:
Hybrid Classes
Fee, excluding tax (in INR)
₹ 42,000
+ GST: ₹7,560
Convert to USD
Next batch begins from:
In Progress...
Seat Availability
(Classroom full)
Enterprise
Have a team of 25 or more team members and need a custom training plan?
Contact Us
or call
(+91) 988.011.2117
Certificate in Data Analytics & Data Science.
Based on your performance you either get a Certificate of Completion or Certificate of Excellence after successful completion of the course. Choose your schedule.
Certificate of Excellence in Data Analytics & Data Science
Certificate of Completion in Data Analytics & Data Science
Sharpen your Axe.
Use Lab Exercises, Projects, Coding Competitions, and Hackathons as opportunities to practice and apply your programming skills in real-world scenarios.
Exercises
To augment your proficiency in Data Analytics & Data Science, you will be regularly challenged with various assignments like coding challenges, algorithmic puzzles, trick logical, analytical, and mathematical problems - designed to enable you to think creatively and outside the box.
Expect 4 levels of difficulty in these assignments:
- Beginner: Exercises that test your understanding of the subject.
- Intermediate: Exercises that add new and thought-provoking information to the subject.
- Advanced: Exercises that are intended to challenge you.
- Expert: Exercises that are extremely difficult by comparison with most others.
Projects
The projects are designed to ensure that you not only understand the theoretical concepts of Data Analytics & Data Science but also gain hands-on experience in applying those concepts to real-world scenarios. You’ll be required to collaborate as a team to attempt some projects while working as a Lone Wolf / individual contributor on others.
- Two Cornerstone projects that will reinforce in you the strong foundational knowledge of Python Programming Language.
- One Keystone projects will be more challenging, building on the knowledge you have gained through the Cornerstone projects.
- One Capstone project, which will be the culmination of your learning experience in this course.
Competitions
Competitions and Hackathons are a great way to reinforce your learning and to challenge you to apply your skills to real-world scenarios. By participating in these events, you will gain practical experience and develop your problem-solving skills. You will be participating in a variety of events, including but not limited to:
- Sprint Coding Competitions
- Marathon Hackathons
- Catch-the-Flag (CTF) challenges
- War Games
In some competitions, you will collaborate as a team, which will aid you in developing your teamwork skills. In other competitions, you will participate as a Lone Wolf, which will challenge you to think independently and to rely on your own skills and knowledge.
Ace that Data Analytics Interview.
As a part of this course, you’ll learn to crack Data Analytics & Data Sciences interviews. You’ll be thoroughly trained using:
- Mock Interviews: We’ll simulate the pressure and format of a real coding interview, allowing you to practice and improve you technical, communication, and presentation skills under similar conditions.
- Through Whiteboarding, you’ll learn to visually represent your thought process on a physical or a digital whiteboard.
- We put a lot of emphasis on you having a strong understanding of Data Structures, Algorithms, and Algorithm Analysis to efficiently solve complex problems and optimize solutions during a coding interview. which are key factors that interviewers evaluate to assess a candidate’s technical competence.
- You’ll learn how to handle Impossible Questions & Kobayashi Maru situations which are essential in a coding interview to show adaptability, creativity, and resilience under pressure.
- Learn to solve Algorithmic, and Mathematical and Puzzles using Python Programming Language and develop a creative and analytical mindset, logical reasoning, and problem-solving skills that are crucial in cracking a coding interview.
Who can apply?
Eligibility Criteria.
Age Group
For people between the age group 14 - 114 years! Although there is no right age to start learning, the batches may be segregated based on age range or number of years of work experience, if required.
Edu. Qualification
No fancy degrees, no prior experience, no secret handshake required - just bring your curiosity and a sharp eye for detail. If Watson could keep up with Holmes without a detective diploma, so can you.
Gender
Ace Hacker is a gender-neutral learning (and working) environment. Participants may choose any gender they identify with (male/female/non-binary) or choose not to disclose their gender at the time of registration.
Location
Join from anywhere across the globe. Ace Hacker is a Hybrid / Hyflex Classroom learning environment. The classroom includes a mix of students who are present onsite and those who wish to join the class virtually.
Ace Hacker Classroom.
What to expect.
Looking for us?
Ace Hacker,
3rd Floor, No. 670,
RBI Layout Main Road,
J.P. Nagar 7th Phase,
Bengaluru - 560 078
Karnataka, India
BYOD
Bring your own device. Click here for instructions to download and install Python on your machine. You will have access to a fast internet connection, an amazing teacher, and yummy snacks with chai & coffee in the class.
Your Teacher
Your teacher’s enthusiasm is infectious, and you can’t help but feel the same passion for Data Analytics & Data Science as they do. Under their guidance, you will not only learn the technical skills you need to succeed, but you will also develop a love for the subject that will stay with you for years to come.
Be punctual
Be on time or before time if you’d like to start your class with some light snacks and beverages (in case you are attending an onsite session). The door closes sharp on time and entry into the class after the class commences may not be allowed.
Schedule
Classes are in progress.
| Available on | Format | Timing | Starts on | Seat Availability | Fee (INR) | |
|---|---|---|---|---|---|---|
| Weekdays (Mon - Thu) | Hybrid | 4:00 PM - 5:30 PM India Time |
In Progress...
|
(Classroom full) |
₹ 42,000 + ₹ 7,560 (GST) | NOTIFY ME |
| Weekend (Sat, Sun) | Hybrid | 12:30 PM - 3:30 PM India Time |
In Progress...
|
(Classroom full) |
₹ 42,000 + ₹ 7,560 (GST) | NOTIFY ME |
Hire Data Hackers.
Hacker is someone who enjoys exploring the details of computers and how to stretch their capabilities, as opposed to most users who prefer to learn the minimum necessary. ‘Hacker’ is a term of respect, used among computer programmers, designers, and engineers. A hacker is someone who creates original and ingenious programs.
Need Data Hackers for your team?
Contact Us or call (+91) 988.011.2117Questions?
Challenge Ready: You’ll be battle-tested for the world of Data Analytics and Data Science through hands-on mysteries, real-time data challenges, and Sherlock-style deduction scenarios - each crafted to sharpen your instincts, storytelling, and analytical thinking under pressure. With a strong focus on data wrangling, visualization, statistics, and Python-powered insights, you’ll learn to break down messy datasets, spot hidden patterns, and draw razor-sharp conclusions like a true data detective. Elementary? Not quite. Extraordinary? Absolutely.
Demo Weeks: A grand showcase where students present their magical creations to recruiters from tech kingdoms far and wide. Impress the right folks, and you may be summoned for a technical duel (aka interview).
Note: Demo Weeks are organized only when companies are actively recruiting and approach Ace Hacker seeking candidates. While we create the stage, the spotlight depends on real-time industry demand.
Job Board: Students have access to an Ace Hacker alumni platform on an internal Slack channel and a Discord server. This platform serves as a hub for Ace Hacker students seeking job opportunities in the tech industry. Within these channels, regular job openings from various tech companies and startups are posted, providing students with a range of options to choose from. The platform not only offers job listings but also fosters a supportive community by connecting students with mentors - professionals from the companies posting the job offers, alumni of the institute, and career coaches from Ace Hacker. The mentors provide invaluable guidance to students, offering insights on the application process and addressing any queries they may have.
Partner Connect: This program provides companies with the opportunity to hire students from Ace Hacker. Students are notified when a company expresses interest to recruit from Ace Hacker. We connect the applicants with the Recruitment Team of the prospective employers who would then take the process forward with the applicants.
Wish to hire from Ace Hacker?
Contact Us
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- Personalized Interaction: This course offers a dynamic learning environment where questions are asked, discussions takes place, and personalized feedback is provided.
- Synchronous Learning Experience: All students attend the sessions simultaneously. We create a sense of community to foster collaborative learning. We believe that being in-sync with fellow classmates will enhance your learning experience and also allow for active participation.
- Quality and Timeliness: Receive up-to-date information, get immediate clarification of doubts in Q&A section of the live class, in AMA (Ask Me Anything) sessions, and during Office Hours. Office hours are times when you can meet with your instructor to discuss the material being presented in class or other related interests you have. Engage in real-time discussions through these live sessions.
Policy Overview
After attending at least two classes (and before attending the third), if you find that the course does not align with your expectations or goals, we’ll refund the full amount of the paid fees, excluding any non-refundable fees or charges (for example, GST, or any other tax component) specified in the fee structure. This allows you to experience the course content and teaching style before making a final decision.
Refund Timeline
- You are eligible for a refund if you decide to withdraw from the course after completing the second class but before attending the third class.
- Refunds are processed within 14 working days from the receipt of the refund request.
- Please note that it may take a few additional days for the refund to appear in your account, depending on your payment method and financial institution.
- In case you have received any learning materials or resources, please return them in good condition within 5 working days of the refund request. Failure to return the learning materials or resources within the specified time frame may result in a deduction from the refund amount to cover the cost of those materials.
To initiate a refund, please follow these steps:
- Send an email to connect@acehacker.com from the email address associated with your course enrollment. Include your full name, course name, and date of enrollment in the email.
- In the email subject, please use the format: “Course Withdrawal Refund Request | [Course Name]”
- In the email body, please mention your Student ID, Full Name, and Date of commencement of the course.
See our refund policy.
But if you absolutely must miss a session, and it couldn’t be helped, worry not - we’ll slip you the recording, sealed and timestamped, so you can catch up and rejoin the investigation fully briefed.
Reach Out.
Got questions, curiosities, or mysterious clues to decode? Don’t keep them a secret - drop us a line and let’s solve it together.
