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Introduction to Algorithms
by MIT, Spring 2020

Instructor(s) 👨‍🏫

Prof. Erik Demaine
Dr. Jason Ku
Prof. Justin Solomon

🎬 21 lecture video lessons
🎬 3 quiz video lessons (4+ hours)
🎬 8 problem video sessions (12 hours)


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Data Scientist Roadmap
|
|-- 1. Basic Foundations
|   |-- a. Mathematics
|   |   |-- i. Linear Algebra
|   |   |-- ii. Calculus
|   |   |-- iii. Probability
|   |   -- iv. Statistics
|   |
|   |-- b. Programming
|   |   |-- i. Python
|   |   |   |-- 1. Syntax and Basic Concepts
|   |   |   |-- 2. Data Structures
|   |   |   |-- 3. Control Structures
|   |   |   |-- 4. Functions
|   |   |   -- 5. Object-Oriented Programming
|   |   |
|   |   -- ii. R (optional, based on preference)
|   |
|   |-- c. Data Manipulation
|   |   |-- i. Numpy (Python)
|   |   |-- ii. Pandas (Python)
|   |   -- iii. Dplyr (R)
|   |
|   -- d. Data Visualization
|       |-- i. Matplotlib (Python)
|       |-- ii. Seaborn (Python)
|       -- iii. ggplot2 (R)
|
|-- 2. Data Exploration and Preprocessing
|   |-- a. Exploratory Data Analysis (EDA)
|   |-- b. Feature Engineering
|   |-- c. Data Cleaning
|   |-- d. Handling Missing Data
|   -- e. Data Scaling and Normalization
|
|-- 3. Machine Learning
|   |-- a. Supervised Learning
|   |   |-- i. Regression
|   |   |   |-- 1. Linear Regression
|   |   |   -- 2. Polynomial Regression
|   |   |
|   |   -- ii. Classification
|   |       |-- 1. Logistic Regression
|   |       |-- 2. k-Nearest Neighbors
|   |       |-- 3. Support Vector Machines
|   |       |-- 4. Decision Trees
|   |       -- 5. Random Forest
|   |
|   |-- b. Unsupervised Learning
|   |   |-- i. Clustering
|   |   |   |-- 1. K-means
|   |   |   |-- 2. DBSCAN
|   |   |   -- 3. Hierarchical Clustering
|   |   |
|   |   -- ii. Dimensionality Reduction
|   |       |-- 1. Principal Component Analysis (PCA)
|   |       |-- 2. t-Distributed Stochastic Neighbor Embedding (t-SNE)
|   |       -- 3. Linear Discriminant Analysis (LDA)
|   |
|   |-- c. Reinforcement Learning
|   |-- d. Model Evaluation and Validation
|   |   |-- i. Cross-validation
|   |   |-- ii. Hyperparameter Tuning
|   |   -- iii. Model Selection
|   |
|   -- e. ML Libraries and Frameworks
|       |-- i. Scikit-learn (Python)
|       |-- ii. TensorFlow (Python)
|       |-- iii. Keras (Python)
|       -- iv. PyTorch (Python)
|
|-- 4. Deep Learning
|   |-- a. Neural Networks
|   |   |-- i. Perceptron
|   |   -- ii. Multi-Layer Perceptron
|   |
|   |-- b. Convolutional Neural Networks (CNNs)
|   |   |-- i. Image Classification
|   |   |-- ii. Object Detection
|   |   -- iii. Image Segmentation
|   |
|   |-- c. Recurrent Neural Networks (RNNs)
|   |   |-- i. Sequence-to-Sequence Models
|   |   |-- ii. Text Classification
|   |   -- iii. Sentiment Analysis
|   |
|   |-- d. Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU)
|   |   |-- i. Time Series Forecasting
|   |   -- ii. Language Modeling
|   |
|   -- e. Generative Adversarial Networks (GANs)
|       |-- i. Image Synthesis
|       |-- ii. Style Transfer
|       -- iii. Data Augmentation
|
|-- 5. Big Data Technologies
|   |-- a. Hadoop
|   |   |-- i. HDFS
|   |   -- ii. MapReduce
|   |
|   |-- b. Spark
|   |   |-- i. RDDs
|   |   |-- ii. DataFrames
|   |   -- iii. MLlib
|   |
|   -- c. NoSQL Databases
|       |-- i. MongoDB
|       |-- ii. Cassandra
|       |-- iii. HBase
|       -- iv. Couchbase
|
|-- 6. Data Visualization and Reporting
|   |-- a. Dashboarding Tools
|   |   |-- i. Tableau
|   |   |-- ii. Power BI
|   |   |-- iii. Dash (Python)
|   |   -- iv. Shiny (R)
|   |
|   |-- b. Storytelling with Data
|   -- c. Effective Communication
|
|-- 7. Domain Knowledge and Soft Skills
|   |-- a. Industry-specific Knowledge
|   |-- b. Problem-solving
|   |-- c. Communication Skills
|   |-- d. Time Management
|   -- e. Teamwork
|
-- 8. Staying Updated and Continuous Learning
    |-- a. Online Courses
    |-- b. Books and Research Papers
    |-- c. Blogs and Podcasts
    |-- d. Conferences and Workshops
    `-- e. Networking and Community Engagement

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Top 10 Python Concepts
Variables & Data Types

Understand integers, floats, strings, booleans, lists, tuples, sets, and dictionaries.

Control Flow (if, else, elif)
Write logic-based programs using conditional statements.

Loops (for & while)
Automate tasks and iterate over data efficiently.

Functions
Build reusable code blocks with def, understand parameters, return values, and scope.

List Comprehensions
Create and transform lists concisely:
[x*2 for x in range(10) if x % 2 == 0]

Modules & Packages
Import built-in, third-party, or custom modules to structure your code.

Exception Handling
Handle errors using try, except, finally for robust programs.

Object-Oriented Programming (OOP)
Learn classes, objects, inheritance, encapsulation, and polymorphism.

File Handling
Open, read, write, and manage files using open(), read(), write().

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Use powerful libraries like:
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- Pandas for data analysis
- Matplotlib/Seaborn for visualization
- Requests for API calls
- JSON for data parsing

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✅ Data Science Project Series: Part 1 - Loan Prediction.

Project goal
Predict loan approval using applicant data.

Business value
- Faster decisions
- Lower default risk
- Clear interview story

Dataset
Use the common Loan Prediction dataset from analytics practice platforms.

Target
Loan_Status
Y approved
N rejected

Tech stack
- Python
- Pandas
- NumPy
- Matplotlib
- Seaborn
- Scikit-learn

Step 1. Import libraries

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.model_selection import train_test_split
from sklearn.preprocessing import LabelEncoder
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import accuracy_score, confusion_matrix, classification_report

Step 2. Load data

df = pd.read_csv("loan_prediction.csv")
df.head()

Step 3. Basic checks

df.shape
df.info()
df.isnull().sum()

Step 4. Data cleaning

Fill missing values

df['LoanAmount'].fillna(df['LoanAmount'].median(), inplace=True)
df['Loan_Amount_Term'].fillna(df['Loan_Amount_Term'].mode()[0], inplace=True)
df['Credit_History'].fillna(df['Credit_History'].mode()[0], inplace=True)
categorical_cols = ['Gender','Married','Dependents','Self_Employed']
for col in categorical_cols:
    df[col].fillna(df[col].mode()[0], inplace=True)

Step 5. Exploratory Data Analysis

Credit history vs approval

sns.countplot(x='Credit_History', hue='Loan_Status', data=df)
plt.show()
Income distribution.python
sns.histplot(df['ApplicantIncome'], kde=True)
plt.show()

Insight
Applicants with credit history have far higher approval rates.

Step 6. Feature engineering
Create total income.

df['TotalIncome'] = df['ApplicantIncome'] + df['CoapplicantIncome']

# Log transform loan amount
df['LoanAmount_log'] = np.log(df['LoanAmount'])

Step 7. Encode categorical variables

le = LabelEncoder()
for col in df.select_dtypes(include='object').columns:
    df[col] = le.fit_transform(df[col])

Step 8. Split features and target

X = df.drop('Loan_Status', axis=1)
y = df['Loan_Status']
X_train, X_test, y_train, y_test = train_test_split(
    X, y, test_size=0.3, random_state=42
)

Step 9. Build model
Logistic Regression.

model = LogisticRegression(max_iter=1000)
model.fit(X_train, y_train)

Step 10. Predictions

y_pred = model.predict(X_test)

Step 11. Evaluation

accuracy = accuracy_score(y_test, y_pred)
print("Accuracy:", accuracy)
confusion_matrix(y_test, y_pred)
Classification report.python
print(classification_report(y_test, y_pred))

Typical result
- Accuracy around 80 percent
- Strong precision for approved loans
- Recall needs focus for rejected loans

Step 12. Model improvement ideas
- Use Random Forest
- Tune hyperparameters
- Handle class imbalance
- Track recall for rejected cases

Resume bullet example
- Built loan approval prediction model using Logistic Regression
- Achieved ~80 percent accuracy
- Identified credit history as top approval driver

Interview explanation flow
- Start with bank risk problem
- Explain feature impact
- Justify Logistic Regression
- Discuss recall vs accuracy

Double Tap ♥️ For More

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