What you will learn

Master Excel for Data Analysis

• Formulas: Master the art of formula creation for computation, data manipulation, and job automation.
• Pivot Tables: Summarize large datasets with Pivot Tables, analyzing data trends and patterns.

Programming with Python and R

• Python/R: Learn the essential programming languages for data science.

Basics of Data Science

• Basics, Functions, Strings: Learn foundational concepts in Python or R, preparing for advanced data manipulation.

Data Visualization with Tableau and Power BI

• Tableau / Power BI: Master data visualization techniques.

Code wih TLS - Data Science Course Roadmap

Our Data Science course is designed to help you master the necessary skills in Python programming, machine learning, data visualization, and more. Here is a detailed breakdown of the 6-month roadmap:

Data Science Course Breakdown

Month 1: Introduction to Data Science and Python Programming

Week 1-2: Introduction to Data Science

• Overview of Data Science and its applications
• Data Science Process (Data Collection, Data Cleaning, Data Analysis, Modeling, and Visualization)
• Introduction to Python for Data Science
• Basic Python syntax (variables, data types, loops, conditionals)
• Functions, modules, and libraries (NumPy, pandas)

Week 3-4: Data Manipulation and Analysis with Python

• Introduction to NumPy (Arrays, Matrix Operations)
• Introduction to pandas (DataFrames, Series, Indexing, and Slicing)
• Data Cleaning (Handling Missing Data, Data Imputation)
• Data Wrangling (Merging, Joining, Concatenating Data)
• Descriptive Statistics (Mean, Median, Mode, Variance, Standard Deviation)

Month 2: Data Visualization and Statistical Analysis

Week 1-2: Data Visualization Basics

• Introduction to Matplotlib and Seaborn
• Basic Plotting (Line, Bar, Scatter, Histogram, Boxplot)
• Understanding and Creating Heatmaps, Pairplots

Week 3-4: Advanced Visualization and Statistical Analysis

• Introduction to Plotly (Interactive Plots)
• Hypothesis Testing (t-tests, z-tests, Chi-Square test)
• Confidence Intervals and p-values
• Correlation and Causation Analysis

Month 3: Introduction to Machine Learning

Week 1-2: Supervised Learning - Regression

• Linear Regression, Polynomial Regression
• Model Evaluation (Mean Absolute Error, R-squared)

Week 3-4: Supervised Learning - Classification

• Logistic Regression, K-Nearest Neighbors
• Model Evaluation (Confusion Matrix, Precision, Recall, F1-Score)

Month 4: Unsupervised Learning and Clustering

Week 1-2: Clustering Algorithms

• K-Means Clustering, DBSCAN
• Evaluating Clustering Results (Silhouette Score, Elbow Method)

Week 3-4: Dimensionality Reduction and Advanced Topics

• Principal Component Analysis (PCA)
• Feature Engineering (Normalization, Standardization)

Month 5: Deep Learning and Neural Networks

Week 1-2: Introduction to Neural Networks

• Structure of Neural Networks (Neurons, Layers, Activation Functions)
• Introduction to TensorFlow and Keras

Week 3-4: Advanced Neural Networks

• Convolutional Neural Networks (CNNs) for Image Classification
• Recurrent Neural Networks (RNNs) for Time Series and Text Data

Month 6: Capstone Project and Deployment

Week 1-2: Working on a Capstone Project

• Define the Problem Statement (choose a domain: finance, healthcare, e-commerce, etc.)
• Collect and Clean Data
• Apply Data Science Techniques (EDA, Feature Engineering)

Week 3-4: Model Deployment and Presentation

• Introduction to Model Deployment (Flask, Streamlit, FastAPI)
• Deploying a Machine Learning Model as a Web Application

Job Scope in Data Science

Data Science has a huge demand in India, with positions such as Data Analyst, Data Scientist, Machine Learning Engineer, and more. Companies in various sectors like finance, healthcare, and e-commerce are on the lookout for skilled professionals.

Data Science Job Roles

• Data Scientist
• Machine Learning Engineer
• Data Analyst
• Data Engineer
• Business Intelligence Analyst