Professional IT Training Academy • Complete Data Science Course [file:6]
160+ Hours
Beginner to Advanced
Live Projects
100% Job Ready
Certification
Become an Industry-Ready Data Scientist
Learn end‑to‑end Data Science from Python programming to Machine Learning, Deep Learning, NLP, Business Intelligence and Big Data analytics with real‑world projects and case studies. This course is ideal for BCA/B.Tech, B.Sc/M.Sc, working professionals and career switchers who want to build strong foundations and practical skills for Data Science roles. [file:6]
What You’ll Build
End‑to‑End ML Pipeline (Scikit‑learn)
Deep Learning Models (TensorFlow/Keras)
NLP Projects (Text Classification & Chatbots)
Interactive Dashboards (Tableau & Power BI)
Technologies You’ll Master
Complete Data Science & Analytics Stack
Python
NumPy & Pandas
Matplotlib & Seaborn
Scikit‑learn
TensorFlow/Keras
NLP
SQL
R Programming
Tableau
Power BI
Apache Spark
Complete 14-Module Curriculum
1
Introduction to Data Science
- What is Data Science, lifecycle, roles (DA, DS, ML Engineer)
- Applications in finance, healthcare, marketing, and IT
- Types of analytics: descriptive, diagnostic, predictive, prescriptive
- End‑to‑end project workflow overview (CRISP‑DM)
2
Core Python
- Python setup, Jupyter/IDE, syntax, variables, data types
- Control statements, loops, functions, modules, packages
- Data structures: lists, tuples, dicts, sets, comprehensions
- Error handling, basic file handling, mini coding exercises
3
Python for Data Science (Advanced)
- NumPy arrays, vectorization, broadcasting, numerical operations
- Pandas Series & DataFrames, joins, groupby, time series
- Data cleaning, missing values, outliers, data transformation
- Exploratory Data Analysis (EDA) mini project
4
Reporting & Visualization
- Visualization principles, choosing the right chart
- Matplotlib & Seaborn: line, bar, hist, scatter, pairplot, heatmap
- Exporting reports to Excel/PDF, basic report automation
- Visual storytelling with a real dataset report
5
Machine Learning with Scikit‑learn - I
- ML workflow, train/test split, evaluation metrics
- Supervised learning: Linear/Logistic Regression, k‑NN
- Confusion matrix, accuracy, precision, recall, F1‑score
- Classification/regression project with Scikit‑learn
6
Machine Learning with Scikit‑learn - II
- Decision Trees, Random Forest, SVM, Naive Bayes
- Unsupervised learning: K‑Means, Hierarchical clustering, PCA
- Hyperparameter tuning with GridSearchCV/RandomizedSearchCV
- End‑to‑end ML pipeline with model selection and tuning
7
Introduction to Artificial Intelligence (AI)
- AI vs ML vs DL, AI applications & branches
- Search, knowledge representation, and rule‑based systems (overview)
- AI use‑cases in various industries, ethics and limitations
- Simple AI mini‑project / case study discussion
8
Natural Language Processing (NLP)
- Text preprocessing: tokenization, stopwords, stemming, lemmatization
- Bag‑of‑Words, TF‑IDF, word embeddings (overview)
- Text classification (spam/ham, sentiment analysis)
- NLP project using Scikit‑learn / basic deep learning model
9
Deep Learning with TensorFlow/Keras
- Neural network basics: perceptron, activations, loss functions
- Building ANN models with Keras (Sequential & Functional APIs)
- CNN basics for image classification (overview project)
- Training, evaluation, regularization, saving & loading models
10
Data Science with R
- R basics, RStudio, vectors, lists, data frames, factors
- Data import, cleaning, transformation with dplyr & tidyr (overview)
- Data visualization with ggplot2 (overview)
- Small EDA project in R
11
Data Analytics with SQL
- SQL recap: SELECT, WHERE, GROUP BY, HAVING, ORDER BY
- Joins, subqueries, window functions for analytics
- Business queries, KPIs, cohort and trend analysis
- Analytics project using SQL on realistic dataset
12
Data Analytics & Visualization with Tableau
- Tableau interface, data connections, joins, blends
- Charts, filters, parameters, calculated fields
- Dashboards, stories, publishing, sharing insights
- Interactive business dashboard project
13
Data Analytics & Visualization with Power BI
- Power BI Desktop, data import, Power Query, data model
- DAX basics, measures, calculated columns
- Reports, dashboards, slicers, publishing to Power BI service
- Management dashboard project in Power BI
14
Big Data Analysis with Apache Spark
- Big Data concepts, Spark architecture & ecosystem
- RDDs vs DataFrames, Spark SQL basics
- Batch processing & simple ML tasks with Spark MLlib (overview)
- Mini project: analyzing large dataset using Spark (conceptual/lab)
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