Data Science with Python
Master the skills of Data Science with Python with this advanced Data Science course by SGMS Academy. You will get to learn from the working professionals & industry experts with 1:1 mentorship in this intensive online bootcamp.
Only Few Seats Left – No Prior Programming Knowledge Required.
Learning Format
Live classes
Live Classes
200+ Hrs
Career Services
by SGMS Academy
Fee
₹3750(15%)
About Program
This online Data Science with Python course led by the working professionals aims at helping you master all the basic and advanced level skills that are crucial in the field of Data Science.
Key Highlights
- 200+ Hrs of Learning
- All live classes
- 10 Industry projects
- Placement Assistance
- 24*7 Support
- 1:1 Mock Interview
- Flexible timings
- Learn from working professionals
- one-on-one with Industry Mentors
- Resume preparation
- No prior coding knowledge required
- Low-cost.
Free Career Counselling
We are happy to help you 24/7
Who Can Apply for the Course?
- Individuals with a bachelor’s degree and a keen interest to learn Data Science.
- 3rd and Final Year students (All Branches are eligible)
- IT professionals looking for a career transition as Data Scientists.
- Professionals aiming to move ahead in their IT career.
- Artificial Intelligence and Business Intelligence professionals.
- Developers and Project Managers.
- Freshers who aspire to build their career in the field of Data Science.
What roles can a Data Science play?
Junior Data Scientist
A junior data scientist should have the skills required to competently: build datasets, clean and manipulate data, make data accessible to users, perform advanced analytics, do modeling, present data statistics visually.
Senior Data Scientist
Formulating, suggesting, and managing data-driven projects which are geared at furthering the business’s interests. Collating and cleaning data from various entities for later use by junior data scientists.
Applied Scientist
Design and build Machine Learning models to derive intelligence for the numerous services and products offered by the organization.
Business Analyst
Extract data from the respective sources to perform business analysis, and generate reports, dashboards, and metrics to monitor the company’s performance.
Skills to Master
Python
Data Science
Data Analysis
Data Visualization
GIT
Data Wrangling
SQL
Story Telling
Prediction algorithms
Power BI
Interested in This Program? Secure your spot now.
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Curriculum
- Define Data Science.
- Discuss the roles and responsibilities of a Data Scientist.
- List various applications of Data Science.
- Explain Data Science importance.
- Describe Python and its importance
- Describe Data Analytics process and its steps.
- List the skills and tools required for data analysis.
- Understand the challenges of the Data Analytics process.
- Explain Exploratory data analysis technique.
- Illustrate data visualization techniques
- Describe Hypothesis testing
- Differentiate between statistical and non-statistical analysis.
- Illustrate the two major categories of statistical analysis and their differences.
- Describe statistical analysis process.
- Calculate mean, median, mode, and percentile.
- Describe data distribution and the various methods of representing it.
- Explain types of frequencies.
- Outline correlation matrix and its uses.
Introduction to SQL
- Understanding what SQL (Structured Query Language) is and its importance.
- Overview of different SQL databases (MySQL, PostgreSQL, SQLite, etc.).
Database Fundamentals
- Understanding databases, tables, and relationships.
- Difference between SQL and NoSQL databases.
- Basic database concepts: schema, primary keys, foreign keys, indexes.
SQL Basics
- Writing basic SQL queries.
- Using SELECT statements to retrieve data.
- Filtering data with WHERE clauses.
- Sorting data using ORDER BY.
- Limiting results with LIMIT and OFFSET.
Data Manipulation
- Inserting data into tables with INSERT statements.
- Updating existing records using UPDATE statements.
- Deleting records with DELETE statements.
- Using TRUNCATE to quickly clear tables.
Data Retrieval and Aggregation
- Using aggregate functions (COUNT, SUM, AVG, MAX, MIN).
- Grouping data with GROUP BY.
- Filtering groups with HAVING clauses.
- Understanding and using aliases.
Advanced SQL Queries
- Using JOINs to combine data from multiple tables (INNER JOIN, LEFT JOIN, RIGHT JOIN, FULL OUTER JOIN).
- Subqueries and nested queries.
- Using UNION, INTERSECT, and EXCEPT to combine results from multiple queries.
Database Design and Normalization
- Principles of database design.
- Understanding and applying normalization (1NF, 2NF, 3NF).
- Designing and creating database schemas.
Indexing and Performance Optimization
- Understanding indexes and their impact on query performance.
- Creating and managing indexes.
- Best practices for optimizing SQL queries.
Working with SQL in Data Science
- Connecting SQL databases to Python using libraries such as SQLAlchemy and pandas.
- Performing data analysis and manipulation directly within SQL.
- Integrating SQL queries within data science workflows.
SQL Functions and Procedures
- Creating and using SQL functions.
- Writing and executing stored procedures.
- Using triggers for automated database operations.
Data Security and Transactions
- Understanding transactions and ACID properties.
- Implementing transactions with COMMIT and ROLLBACK.
- Managing user permissions and security best practices.
Practical SQL Projects
- Hands-on projects to solidify understanding (e.g., building a database for a retail store, analyzing sales data).
- Real-world scenarios and case studies to apply SQL skills.
Introduction to Power BI
- Overview of Power BI and its components.
- Understanding the Power BI ecosystem (Power BI Desktop, Power BI Service, Power BI Mobile).
- Benefits of using Power BI for data visualization and business intelligence.
Getting Started with Power BI
- Installing and setting up Power BI Desktop.
- Overview of the Power BI interface and main features.
- Connecting to various data sources (Excel, SQL Server, online services, etc.).
Data Preparation and Transformation
- Importing and loading data into Power BI.
- Using Power Query Editor to clean and transform data.
- Merging and appending queries.
- Understanding and applying data transformation steps (e.g., filtering, sorting, and grouping).
Data Modeling
- Creating and managing data models.
- Defining relationships between tables.
- Understanding data types and formatting.
- Creating calculated columns and measures using DAX (Data Analysis Expressions).
Data Analysis with DAX
- Introduction to DAX and its importance in Power BI.
- Basic DAX functions and operators.
- Writing DAX formulas for calculated columns and measures.
- Using DAX for advanced calculations (e.g., time intelligence, conditional calculations).
Creating Interactive Reports
- Building and customizing reports in Power BI.
- Using different types of visualizations (e.g., bar charts, line charts, pie charts, maps).
- Formatting and styling visual elements.
- Adding filters and slicers to enhance interactivity.
Designing Dashboards
- Understanding the difference between reports and dashboards.
- Creating and customizing dashboards in Power BI Service.
- Pinning visualizations and reports to dashboards.
- Organizing and sharing dashboards with others.
Advanced Visualizations and Custom Visuals
- Using advanced visualizations (e.g., scatter plots, waterfall charts, tree maps).
- Importing and using custom visuals from the Power BI marketplace.
- Creating and managing drill-throughs and drill-downs for deeper analysis.
Power BI Service and Collaboration
- Publishing reports to Power BI Service.
- Understanding workspaces, apps, and datasets in Power BI Service.
- Collaborating with team members and sharing reports and dashboards.
- Scheduling data refreshes and managing data gateways.
Power BI Mobile
- Overview of Power BI Mobile app.
- Creating mobile-optimized reports and dashboards.
- Interacting with Power BI content on mobile devices.
Power BI Embedded and API Integration
- Introduction to Power BI Embedded.
- Embedding Power BI reports into applications.
- Using Power BI REST API for automation and customization.
Security and Administration
- Managing user roles and permissions.
- Implementing row-level security (RLS) to control data access.
- Best practices for data governance and compliance.
Practical Power BI Projects
- Hands-on projects to solidify understanding (e.g., building sales dashboards, financial reports).
- Real-world scenarios and case studies to apply Power BI skills.
Introduction to Python
- Overview of Python and its features.
- Understanding the importance of Python in data science and machine learning.
- Setting up the Python environment.
Python Basics
- Writing and running Python scripts.
- Understanding Python syntax and indentation.
- Basic data types (integers, floats, strings, booleans).
Variables and Data Types
- Declaring and using variables.
- Understanding and using different data types.
- Type conversion and casting.
Operators
- Arithmetic operators.
- Comparison operators.
- Logical operators.
- Assignment operators.
- Bitwise operators.
Control Flow
- Conditional statements (if, elif, else).
- Looping constructs (for, while).
- Using break and continue statements.
Data Structures
- Lists: creation, indexing, slicing, and methods.
- Tuples: creation, indexing, and methods.
- Dictionaries: creation, accessing, and methods.
- Sets: creation, methods, and operations.
Functions
- Defining and calling functions.
- Function arguments and return values.
- Understanding scope and lifetime of variables.
- Lambda functions.
File Handling
- Reading from and writing to files.
- Working with different file modes (read, write, append).
- Handling file exceptions.
Exception Handling
- Understanding exceptions and errors.
- Using try, except, else, and finally blocks.
- Creating custom exceptions.
Modules and Packages
- Importing and using modules.
- Creating custom modules.
- Understanding and using packages.
- The importance of modularity in coding.
Comprehensions
- List comprehensions.
- Dictionary comprehensions.
- Set comprehensions.
Object-Oriented Programming (OOP)
- Understanding classes and objects.
- Defining and using methods.
- Concepts of inheritance, polymorphism, encapsulation, and abstraction.
- Using constructors and destructors.
Advanced Python Concepts
- Decorators and their uses.
- Generators and iterators.
- Context managers (with statement).
Practical Python Projects
- Hands-on projects to solidify understanding (e.g., building a simple calculator, creating a basic web scraper).
- Real-world scenarios and case studies to apply Python skills.
Introduction to NumPy
- Understanding what NumPy is and its role in data science.
- Installing and setting up NumPy.
- Overview of NumPy’s advantages over Python lists.
Creating and Manipulating NumPy Arrays
- Creating arrays using
array()
,arange()
,linspace()
, andzeros()
. - Understanding array data types and type casting.
- Creating multi-dimensional arrays (2D, 3D, and higher dimensions).
- Creating arrays using
Basic Array Operations
- Accessing and modifying array elements.
- Array slicing and indexing.
- Using boolean indexing for conditional filtering.
- Performing element-wise operations.
Array Shape and Reshaping
- Understanding array shape and dimension.
- Reshaping arrays with
reshape()
andravel()
. - Transposing arrays and swapping axes.
Array Mathematics
- Basic arithmetic operations (addition, subtraction, multiplication, division).
- Applying universal functions (ufuncs) such as
np.add()
,np.subtract()
,np.multiply()
,np.divide()
. - Using mathematical functions like
np.sqrt()
,np.exp()
,np.log()
, etc.
Statistical Operations
- Calculating statistical measures such as mean, median, standard deviation, and variance.
- Using functions like
np.mean()
,np.median()
,np.std()
,np.var()
. - Understanding and applying axis-based operations.
Broadcasting
- Understanding the concept of broadcasting.
- Applying broadcasting rules for efficient computation.
- Practical examples demonstrating the power of broadcasting.
Array Manipulation Techniques
- Concatenating arrays with
np.concatenate()
,np.vstack()
, andnp.hstack()
. - Splitting arrays with
np.split()
,np.hsplit()
, andnp.vsplit()
. - Stacking and unstacking arrays.
- Concatenating arrays with
Advanced Array Operations
- Sorting arrays with
np.sort()
. - Finding unique elements with
np.unique()
. - Searching and counting elements using
np.where()
,np.count_nonzero()
, andnp.nonzero()
.
- Sorting arrays with
Linear Algebra with NumPy
- Performing dot products and matrix multiplication with
np.dot()
,np.matmul()
. - Solving linear equations with
np.linalg.solve()
. - Eigenvalues and eigenvectors with
np.linalg.eig()
. - Inverting matrices and calculating determinants.
- Performing dot products and matrix multiplication with
Random Number Generation
- Generating random numbers with
np.random
module. - Creating random arrays with
np.random.rand()
,np.random.randint()
, etc. - Setting random seeds for reproducibility.
- Generating random numbers with
File I/O with NumPy
- Reading from and writing to files using
np.loadtxt()
,np.savetxt()
,np.genfromtxt()
, andnp.save()
.
- Reading from and writing to files using
Practical NumPy Projects
- Hands-on projects to solidify understanding (e.g., implementing basic image processing, performing statistical data analysis).
- Real-world scenarios and case studies to apply NumPy skills.
Introduction to SciPy
- Understanding what SciPy is and its role in scientific computing.
- Installing and setting up SciPy.
- Overview of the SciPy ecosystem and its integration with NumPy.
Basic Functions and Constants
- Using basic functions provided by SciPy.
- Understanding and using constants from
scipy.constants
.
Scientific and Mathematical Functions
- Working with special functions using
scipy.special
. - Applying functions like gamma, beta, erf, and others in scientific calculations.
- Working with special functions using
Optimization and Root Finding
- Understanding optimization problems and methods.
- Using
scipy.optimize
for minimizing functions (minimize()
,curve_fit()
). - Finding roots of equations with
root()
andfsolve()
.
Interpolation
- Understanding interpolation and its applications.
- Performing 1-D interpolation with
scipy.interpolate.interp1d()
. - Using spline interpolation with
scipy.interpolate
functions.
Integration
- Integrating functions using
scipy.integrate.quad()
,dblquad()
, andtplquad()
. - Solving differential equations with
scipy.integrate.odeint()
andsolve_ivp()
.
- Integrating functions using
Linear Algebra
- Performing linear algebra operations with
scipy.linalg
. - Solving linear systems, decompositions (LU, QR, Cholesky), and matrix operations.
- Eigenvalue problems and matrix inversion.
- Performing linear algebra operations with
Signal Processing
- Understanding the basics of signal processing.
- Using
scipy.signal
for filtering, convolution, and signal transformation. - Applying Fourier transforms with
fft()
and related functions.
Statistics and Probability
- Descriptive statistics with
scipy.stats
(mean, median, mode, variance). - Probability distributions (normal, binomial, Poisson) and related functions.
- Performing hypothesis tests (t-test, chi-square test) and calculating p-values.
- Descriptive statistics with
File I/O
- Reading and writing scientific data formats using
scipy.io
. - Working with MATLAB files (
loadmat()
,savemat()
). - Handling other formats like WAV files.
- Reading and writing scientific data formats using
Spatial Data and Image Processing
- Working with spatial data using
scipy.spatial
. - Performing tasks like Delaunay triangulation, KD-trees, and distance computations.
- Basic image processing with
scipy.ndimage
.
- Working with spatial data using
Sparse Matrices
- Understanding sparse matrices and their advantages.
- Creating and manipulating sparse matrices with
scipy.sparse
. - Performing operations and solving linear systems with sparse matrices.
Practical SciPy Projects
- Hands-on projects to solidify understanding (e.g., optimizing a cost function, signal processing applications, solving differential equations).
- Real-world scenarios and case studies to apply SciPy skills.
Introduction to Pandas
- Understanding what Pandas is and its role in data manipulation and analysis.
- Installing and setting up Pandas.
- Overview of Pandas’ data structures: Series and DataFrame.
Data Structures in Pandas
- Creating and understanding Series.
- Creating and understanding DataFrames.
- Differences between Series and DataFrames.
Data Importing and Exporting
- Reading data from various formats (CSV, Excel, JSON, SQL, etc.) using functions like
read_csv()
,read_excel()
, andread_json()
. - Exporting data to different formats using functions like
to_csv()
,to_excel()
, andto_json()
.
- Reading data from various formats (CSV, Excel, JSON, SQL, etc.) using functions like
Data Inspection and Exploration
- Inspecting data using functions like
head()
,tail()
,info()
, anddescribe()
. - Accessing data using
loc[]
andiloc[]
. - Exploring data with statistical summary functions.
- Inspecting data using functions like
Data Cleaning and Preprocessing
- Handling missing data with
isnull()
,dropna()
, andfillna()
. - Removing duplicates with
drop_duplicates()
. - Data type conversions with
astype()
. - Renaming columns and indices with
rename()
.
- Handling missing data with
Data Transformation
- Applying functions to data using
apply()
,map()
, andapplymap()
. - Using
groupby()
for grouping data and performing aggregate functions. - Merging and joining DataFrames using
merge()
,join()
, andconcat()
.
- Applying functions to data using
Data Aggregation and Group Operations
- Aggregating data with functions like
sum()
,mean()
,count()
, andagg()
. - Grouping data with
groupby()
and applying aggregate functions. - Pivot tables with
pivot_table()
.
- Aggregating data with functions like
Time Series Analysis
- Working with date and time data using
to_datetime()
. - Resampling time series data with
resample()
andasfreq()
. - Performing rolling and expanding window calculations.
- Working with date and time data using
Advanced Data Manipulation
- Using multi-indexing for hierarchical indexing.
- Reshaping data with
stack()
,unstack()
,melt()
, andpivot()
. - Applying complex data transformations with
pipe()
.
Data Visualization with Pandas
- Creating basic plots with Pandas’ built-in plotting capabilities using
plot()
. - Customizing plots (titles, labels, legends).
- Integrating with other visualization libraries (Matplotlib, Seaborn).
- Creating basic plots with Pandas’ built-in plotting capabilities using
Performance Optimization
- Understanding the importance of performance in data manipulation.
- Using efficient data structures (e.g., categorical data).
- Optimizing operations with vectorization and avoiding loops.
Practical Pandas Projects
- Hands-on projects to solidify understanding (e.g., data cleaning and analysis of a real dataset, creating pivot tables and summary reports).
- Real-world scenarios and case studies to apply Pandas skills.
Introduction to scikit-learn
- Understanding what scikit-learn is and its role in machine learning.
- Installing and setting up scikit-learn.
- Overview of scikit-learn’s key features and modules.
Basic Concepts of Machine Learning
- Understanding supervised and unsupervised learning.
- Overview of key machine learning concepts: models, training, testing, and evaluation.
Data Preprocessing and Feature Engineering
- Handling missing values with
SimpleImputer
andKNNImputer
. - Encoding categorical variables using
LabelEncoder
andOneHotEncoder
. - Feature scaling and normalization with
StandardScaler
,MinMaxScaler
, andRobustScaler
. - Feature selection techniques using
SelectKBest
andRFE
.
- Handling missing values with
Splitting Data
- Splitting datasets into training and testing sets with
train_test_split()
. - Using cross-validation for model validation with
cross_val_score()
andStratifiedKFold
.
- Splitting datasets into training and testing sets with
Supervised Learning Algorithms
- Linear regression using
LinearRegression
. - Logistic regression using
LogisticRegression
. - Support Vector Machines (SVM) with
SVC
andSVR
. - Decision trees with
DecisionTreeClassifier
andDecisionTreeRegressor
. - Random forests with
RandomForestClassifier
andRandomForestRegressor
. - Gradient boosting with
GradientBoostingClassifier
andGradientBoostingRegressor
. - k-Nearest Neighbors (k-NN) with
KNeighborsClassifier
andKNeighborsRegressor
.
- Linear regression using
Unsupervised Learning Algorithms
- Clustering with
KMeans
,DBSCAN
, andAgglomerativeClustering
. - Dimensionality reduction with
PCA
andt-SNE
. - Anomaly detection with
IsolationForest
andEllipticEnvelope
.
- Clustering with
Model Evaluation and Metrics
- Evaluating classification models using metrics like accuracy, precision, recall, F1 score, and ROC-AUC.
- Evaluating regression models using metrics like mean squared error (MSE), mean absolute error (MAE), and R-squared.
- Confusion matrix and its interpretation.
Hyperparameter Tuning
- Understanding the importance of hyperparameters in machine learning models.
- Performing grid search with
GridSearchCV
. - Performing random search with
RandomizedSearchCV
.
Pipeline and Model Persistence
- Creating machine learning pipelines with
Pipeline
to streamline preprocessing and model training. - Saving and loading trained models with
joblib
andpickle
.
- Creating machine learning pipelines with
Advanced Topics in scikit-learn
- Working with ensemble methods like
BaggingClassifier
,VotingClassifier
, andStackingClassifier
. - Implementing custom transformers and models.
- Handling imbalanced datasets with
SMOTE
and other techniques.
- Working with ensemble methods like
Practical scikit-learn Projects
- Hands-on projects to solidify understanding (e.g., building a predictive model for house prices, classifying customer churn, clustering customer segments).
- Real-world scenarios and case studies to apply scikit-learn skills.
Linear Regression
- Understanding linear regression and its applications.
- Working with simple linear regression and multiple linear regression.
- Interpretation of coefficients and model evaluation metrics (R-squared, MSE, MAE).
Logistic Regression
- Understanding logistic regression for binary classification.
- Sigmoid function and logistic loss.
- Model evaluation metrics (accuracy, precision, recall, F1 score, ROC-AUC).
Decision Trees
- Understanding decision trees and their structure.
- Entropy and information gain for splitting criteria.
- Handling categorical and numerical features.
- Overfitting and methods to prevent it (pruning, minimum samples per leaf).
Random Forests
- Ensemble learning and bagging.
- Building and tuning random forest models.
- Feature importance and visualization.
- Handling missing values and outliers.
Gradient Boosting Machines (GBM)
- Understanding boosting and gradient boosting.
- Building gradient boosting models with libraries like XGBoost, LightGBM, and CatBoost.
- Hyperparameter tuning for gradient boosting models.
- Handling imbalanced datasets with gradient boosting.
Support Vector Machines (SVM)
- Understanding SVM for both classification and regression.
- Kernel trick and non-linear decision boundaries.
- Choosing the appropriate kernel (linear, polynomial, radial basis function).
- Model interpretation and visualization.
k-Nearest Neighbors (k-NN)
- Understanding the k-NN algorithm.
- Choosing the optimal value of k.
- Working with distance metrics (Euclidean distance, Manhattan distance).
- Handling high-dimensional data and scaling features.
Clustering Algorithms
- Understanding unsupervised clustering algorithms (KMeans, DBSCAN, Hierarchical clustering).
- Evaluating clustering results with metrics like silhouette score and Davies-Bouldin index.
- Visualizing clusters with dimensionality reduction techniques like PCA and t-SNE.
Neural Networks
- Introduction to artificial neural networks (ANN).
- Building and training neural networks with libraries like TensorFlow and Keras.
- Understanding different network architectures (feedforward, convolutional, recurrent).
- Hyperparameter tuning for neural networks.
Dimensionality Reduction Techniques
- Understanding the curse of dimensionality.
- Principal Component Analysis (PCA) for dimensionality reduction.
- t-Distributed Stochastic Neighbor Embedding (t-SNE) for visualization.
- Linear Discriminant Analysis (LDA) for feature extraction and dimensionality reduction.
Anomaly Detection Algorithms
- Understanding anomaly detection and its applications.
- Gaussian Mixture Models (GMM) for anomaly detection.
- Isolation Forest for detecting outliers.
- Local Outlier Factor (LOF) for detecting anomalies in high-dimensional data.
Reinforcement Learning
- Introduction to reinforcement learning (RL) and its components (agent, environment, reward).
- Q-learning and deep Q-learning (DQN) for model-free RL.
- Policy gradient methods for model-based RL.
Natural Language Processing (NLP)
- Introduction to NLP and its applications.
- Text preprocessing techniques (tokenization, stemming, lemmatization).
- Building text classification models using algorithms like Naive Bayes, SVM, and neural networks.
- Named Entity Recognition (NER) and sentiment analysis.
Time Series Forecasting
- Introduction to time series forecasting and its applications.
- ARIMA models for univariate time series forecasting.
- Seasonal decomposition and trend analysis.
- Long Short-Term Memory (LSTM) networks for sequence prediction.
Ensemble Learning
- Understanding ensemble learning and its advantages.
- Voting classifiers and regressors.
- Bagging and boosting algorithms (AdaBoost, Gradient Boosting).
- Stacking and blending techniques.
Model Evaluation and Selection
- Understanding cross-validation techniques (k-fold, stratified k-fold, leave-one-out).
- Model selection criteria (bias-variance tradeoff, Occam’s razor).
- Hyperparameter tuning using grid search and random search.
Introduction to Natural Language Processing (NLP)
- Understanding what NLP is and its applications.
- Introduction to text data and its challenges.
- Overview of the NLP pipeline.
Text Preprocessing
- Tokenization: Breaking text into words or tokens.
- Removing punctuation, stopwords, and non-alphanumeric characters.
- Stemming and Lemmatization to reduce words to their base forms.
Feature Extraction
- Bag-of-Words (BoW) model: Converting text data into numerical feature vectors.
- TF-IDF (Term Frequency-Inverse Document Frequency): Weighing terms based on their frequency and importance in documents.
Text Vectorization with scikit-learn
- Using
CountVectorizer
to convert text documents into a matrix of token counts. - Using
TfidfVectorizer
for TF-IDF vectorization. - Customizing vectorization parameters like n-grams, stopwords, and vocabulary size.
- Using
Text Classification
- Understanding text classification tasks (e.g., sentiment analysis, spam detection, topic categorization).
- Training text classifiers using scikit-learn’s
MultinomialNB
,LogisticRegression
, andSVM
models. - Evaluating classifier performance using metrics like accuracy, precision, recall, and F1-score.
Text Clustering
- Introduction to text clustering and its applications.
- Using KMeans clustering for unsupervised text clustering.
- Evaluating clustering performance using metrics like silhouette score and Davies-Bouldin index.
Named Entity Recognition (NER)
- Understanding NER and its importance in information extraction.
- Using scikit-learn’s
NER
module to recognize named entities (e.g., person names, locations, organizations) in text.
Text Similarity and Matching
- Computing similarity between text documents using techniques like cosine similarity and Jaccard similarity.
- Using scikit-learn’s
pairwise_distances
function to calculate pairwise distances between documents.
Sentiment Analysis
- Introduction to sentiment analysis and its applications.
- Training sentiment analysis models using scikit-learn classifiers.
- Understanding sentiment lexicons and dictionaries.
Topic Modeling
- Introduction to topic modeling and its applications (e.g., document clustering, content recommendation).
- Implementing Latent Dirichlet Allocation (LDA) for topic modeling using scikit-learn’s
LatentDirichletAllocation
class.
Text Feature Engineering
- Creating custom text features such as word embeddings using techniques like Word2Vec and GloVe.
- Using pre-trained word embeddings for feature representation.
Handling Imbalanced Text Data
- Understanding imbalanced text data and its challenges.
- Techniques for handling imbalanced classes (e.g., class weighting, resampling).
Hyperparameter Tuning
- Tuning text classification and clustering models using grid search and cross-validation.
- Optimizing vectorization parameters and model hyperparameters for improved performance.
Model Interpretability
- Interpreting text classification models using techniques like feature importance and model-agnostic explanations.
- Visualizing model predictions and decision boundaries.
Real-World Applications and Case Studies
- Exploring real-world applications of NLP in various domains (e.g., healthcare, finance, social media).
- Analyzing case studies and practical examples of NLP projects using scikit-learn.
Introduction to Data Visualization
- Understanding the importance of data visualization in data analysis and storytelling.
- Overview of popular data visualization libraries in Python: Matplotlib and Plotly.
Basic Plotting with Matplotlib
- Introduction to Matplotlib’s
pyplot
interface. - Creating line plots, scatter plots, bar plots, and histograms.
- Customizing plots with labels, titles, colors, and markers.
- Introduction to Matplotlib’s
Advanced Plotting with Matplotlib
- Creating subplots and multi-panel figures.
- Adding annotations, legends, and text to plots.
- Plotting categorical data with bar charts and pie charts.
- Creating 3D plots and contour plots.
Interactive Visualization with Plotly
- Introduction to Plotly and its advantages for interactive data visualization.
- Creating interactive line plots, scatter plots, and bar plots with Plotly Express.
- Customizing Plotly plots with layout options, annotations, and hover information.
Plotly Dash for Web Applications
- Introduction to Plotly Dash for building interactive web applications.
- Creating Dash layouts with HTML components and Plotly graphs.
- Adding interactivity with Dash callbacks and event handling.
Statistical Visualization with Seaborn
- Overview of Seaborn for statistical data visualization.
- Creating statistical plots like box plots, violin plots, and pair plots.
- Visualizing relationships between variables with scatter plots and regression plots.
Geospatial Visualization with Plotly
- Introduction to Plotly Geo plots for visualizing geospatial data.
- Creating choropleth maps, bubble maps, and scattergeo plots.
- Customizing map layouts, colors, and markers.
Time Series Visualization
- Visualizing time series data with Matplotlib and Plotly.
- Creating time series line plots, area plots, and candlestick plots.
- Adding interactive elements like sliders and date selectors to time series plots.
Customizing Visualizations
- Customizing plot styles and themes.
- Using color palettes and color maps effectively.
- Adding annotations, text, and shapes to highlight important information.
Visual Storytelling and Dashboarding
- Designing effective visualizations for storytelling and presentation.
- Creating dashboards and reports with multiple interactive visualizations.
- Using storytelling techniques to convey insights and narratives through visualizations.
Performance Optimization and Best Practices
- Optimizing plot performance for large datasets.
- Following best practices for clean and effective data visualization.
- Leveraging Matplotlib and Plotly’s documentation and community resources.
Real-World Applications and Case Studies
- Exploring real-world applications of data visualization in various domains (e.g., finance, healthcare, marketing).
- Analyzing case studies and practical examples of data visualization projects using Matplotlib and Plotly.
Introduction to Web Scraping
- Understanding what web scraping is and its applications.
- Overview of HTML structure and elements.
Setting Up the Environment
- Installing necessary libraries: Beautiful Soup, Requests.
- Setting up a virtual environment for web scraping projects.
Basic HTML Structure
- Understanding HTML tags, attributes, and elements.
- Inspecting HTML structure using web browser developer tools.
Introduction to Beautiful Soup
- Overview of Beautiful Soup and its features.
- Parsing HTML content with Beautiful Soup.
Navigating HTML Trees
- Using Beautiful Soup to navigate HTML trees.
- Accessing tags, attributes, and text content.
Finding and Selecting Elements
- Using methods like
find()
andfind_all()
to locate specific elements. - Navigating the DOM tree to locate nested elements.
- Using methods like
Extracting Data
- Extracting text content, attributes, and URLs from HTML elements.
- Handling different types of data (text, links, images).
Handling Dynamic Content
- Understanding dynamic content and AJAX requests.
- Using tools like Selenium for scraping dynamic content.
Scraping Multiple Pages
- Implementing pagination and scraping multiple pages.
- Iterating through multiple pages using loops and recursion.
Parsing and Cleaning Data
- Parsing extracted data and converting it into structured formats (e.g., JSON, CSV).
- Cleaning and preprocessing scraped data (removing HTML tags, whitespace, etc.).
Handling Errors and Exceptions
- Handling common web scraping errors (e.g., connection errors, timeouts).
- Implementing error handling and retry mechanisms.
Respecting Robots.txt and Terms of Service
- Understanding robots.txt files and their role in web scraping ethics.
- Following website terms of service and usage policies.
Advanced Techniques
- Scraping data from JavaScript-rendered websites using tools like Splash.
- Scraping data from APIs and JSON endpoints.
- Implementing advanced scraping techniques like web scraping with proxies and user agents.
Ethical and Legal Considerations
- Understanding the ethical implications of web scraping.
- Respecting website terms of service and copyright laws.
- Avoiding scraping sensitive or personal data.
Real-World Applications and Case Studies
- Exploring real-world applications of web scraping in various domains (e.g., e-commerce, news, research).
- Analyzing case studies and practical examples of web scraping projects.
Interested in This Program? Secure your spot now.
The application is free and takes only 5 minutes to complete.
Projects
Projects will be a part of your Certification in Data Science to consolidate your learning. It will ensure that you have real-world experience in Data Science.
- Practice 20+ Essential Tools
- Designed by Industry Experts
- Get Real-world Experience
Description: In this project, students will analyze data related to the Chandrayaan-3 lunar mission, focusing on various aspects of the mission such as trajectory data, sensor readings, and mission outcomes. Using Python and relevant data science libraries, participants will learn how to process, clean, and visualize space mission data. They will also perform predictive analysis to assess potential mission success factors and identify areas for improvement in future missions.
Description: This project involves analyzing global and regional Covid-19 data to track the spread of the virus, understand its impact, and predict future trends. Students will work with time-series data to create models that forecast infection rates and analyze the effectiveness of various containment measures. The project will cover data preprocessing, exploratory data analysis, visualization, and machine learning techniques such as regression and time-series forecasting.
Description: Students will develop a machine learning model to detect fraudulent credit card transactions. Using a dataset of credit card transactions, they will apply techniques like data balancing, feature engineering, and various classification algorithms (e.g., logistic regression, decision trees, and random forests) to identify and flag suspicious activities. The project emphasizes the importance of accuracy, precision, and recall in fraud detection systems.
Description: In this project, students will work with physiological data (e.g., heart rate, skin conductance) to detect stress levels in individuals. They will preprocess and analyze the data to extract relevant features, then use machine learning models such as support vector machines (SVM) and neural networks to classify stress levels. The project highlights the application of data science in health and wellness, showcasing how machine learning can contribute to personal health monitoring.
Description: Students will build predictive models to forecast the demand for products in a retail environment. Using historical sales data, they will apply time-series analysis and regression techniques to predict future demand. The project will cover various stages of data handling, including data cleaning, feature selection, model training, and validation. Students will learn how to handle seasonality and trends in sales data to make accurate predictions.
Description: This project focuses on analyzing public sentiment towards the Pfizer Covid-19 vaccine using social media data. Students will collect tweets and other social media posts, preprocess the text data, and apply natural language processing (NLP) techniques to classify sentiments (positive, negative, neutral). They will use machine learning algorithms such as Naive Bayes, SVM, and deep learning models to analyze the sentiment and visualize the results.
Description: In this project, students will analyze the sentiment of social media posts about the popular TV series “Squid Game.” They will gather data from platforms like Twitter, preprocess the text, and perform sentiment analysis using NLP techniques. The project will involve training machine learning models to detect sentiment and visualizing the public’s reaction to different episodes or characters, providing insights into the show’s reception.
Description: Students will analyze birth rate data from various countries to identify trends and factors influencing birth rates. They will use statistical analysis and data visualization techniques to explore the relationships between birth rates and socio-economic indicators such as income, education, and healthcare access. The project aims to provide a comprehensive understanding of demographic changes and their implications for policy-making.
Description: This project allows students to explore data science applications across different domains such as healthcare, finance, marketing, and sports. Students will choose a domain-specific dataset, define a problem, and apply data science techniques to provide solutions. This project emphasizes the versatility of data science skills and encourages students to tailor their approach based on the specific requirements and challenges of each domain.
Description: Students will build models to predict the number of followers for social media accounts based on various features such as post frequency, engagement metrics, and content type. Using historical data from platforms like Instagram or Twitter, they will apply regression techniques to forecast follower growth. The project covers data preprocessing, feature engineering, model selection, and evaluation, providing insights into social media dynamics and growth strategies.
Reviews
⭐⭐⭐⭐⭐ (1,213)
Manas Ranjan
I'm Happy to enrolled in this data science program. The syllabus is organized and the course is well designed. Best features are the 24*7 support and trainers who are domain experts.
Afsana Zaman
Great learning experience with this course. The support team was always available. the collaboration of practical with theoretical knowledge makes it highly suitable for those who want to upskill.
Vikanth Singh
It was a wonderful learning experience to learn from the trainers at SGMS Acadmy. They were hands-on and provided real-time scenarios. it is the right place to learn technologies.
Adarsh Vijay
Best data science course with Placements. I was able to upgrade my skills with the help of the rich content and expert training by Instructors who carried good experience in the domains.
Anoop Prasad
The training and support team ae highly cooperative. the best thing about it is the prompt support. The trainers are well versed with the concepts and great content.
Career Services
Interview Preparation
Mock Interview Preparation
Students will go through a number of mock interviews conducted by technical experts who will then offer tips and constructive feedback for reference and improvement. (after 90% of the course completion.)
1 on 1 Career Mentoring Sessions
Attend one-on-one sessions with career mentors on how to develop the required skills and attitude to secure a dream job based on a learners’ educational background, past experience, and future career aspirations. (After 90% of the course completion.)
Job Assistance
Placement Assistance
Placement opportunities are provided once the learner is moved to the placement pool. Get noticed by our 600+ hiring partners. (After 100% of the course completion.)
Exclusive access to our Job portal
Exclusive access to our dedicated job portal and apply for jobs. More than 600 hiring partners’ including top start-ups and product companies hiring our learners. Mentored support on job search and relevant jobs for your career growth.
Profile Building
Career Oriented Sessions
Over 10+ live interactive sessions with an industry expert to gain knowledge and experience on how to build skills that are expected by hiring managers. These will be guided sessions and that will help you stay on track with your up skilling objective.
Resume & LinkedIn Profile Building
Get assistance in creating a world-class resume & Linkedin Profile from our career services team and learn how to grab the attention of the hiring manager at profile shortlisting stage
Our Alumni Works At
Interested in This Program? Secure your spot now.
The application is free and takes only 5 minutes to complete.
Frequently Asked Questions
Upon completion of the Data Science with Python training course and execution of the various projects in this program, you will receive the Certificate.
If you fail to attend any of the live lectures, you will get a copy of the recorded session in the next 12 hours. Moreover, if you have any other queries, you can get in touch with our course advisors or post them on our community.
To be eligible for getting into the placement pool, the learner has to complete the course along with the submission of all projects and assignments. After this, he/she has to clear the PRT (Placement Readiness Test) to get into the placement pool and get access to our job portal as well as the career mentoring sessions.
It’s life time accessible
What is included in this course?
- Non-biased career guidance
- Counselling based on your skills and preference
- No repetitive calls, only as per convenience
- Rigorous curriculum designed by industry experts
- Complete this program while you work
Interested in This Program? Secure your spot now.
The application is free and takes only 5 minutes to complete.