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DATA SCIENCE Course Details
 

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Batch Date: Aug 12th @ 8:30PM

Faculty: Mrs. Sasmitha

Duration: 45 Days

Venue :
DURGA SOFTWARE SOLUTIONS at Maitrivanam
Plot No : 202, IInd Floor ,
HUDA Maitrivanam,
Ameerpet, Hyderabad-500038.

Ph.No: +91 - 9246212143, 80 96 96 96 96


Syllabus:

DATA SCIENCE

Module - 1 (Python Basics)

Welcome To The Course

  • Introduction To DataScience
  • Real Time UseCases Of DataScience
  • Who is a DataScientist??
  • Github Tutorial
  • Skillsets needed for DataScientist
  • 6 Steps to take in 3 Months for a complete transformation to DataScience from any other domain
  • Machine Learning-Giving Computers The ability to learn from data
  • Supervised vs Unsupervised
  • DeepLearning vs Machine Learning
  • Link to get Free Data to Practice?
  • Some Great self Learning DataScience Resources(Books,Tutorials,Vedios,Papers)

Python Fundamentals

  • Software Installation
  • Introduction To Python
  • “Hello Python Program” in IDLE
  • Jupyter Notebook Tutorial
  • Spyder Tutorial
  • Introduction to Python
  • Variable,Operators,DataTypes
  • If Else,For and While Loops
  • Functions
  • Lambda Expression
  • Filter, Map,Reduce
  • Taking input from keyboard
  • HANDS ON-
  • INTERVIEW QUESTION DISCUSSION

Module - 2 (Python Advance)

NumPy

  • Create Arrays
  • Array Item Selection and Indexing
  • Array Mathematics
  • Array Operation
  • HANDS ON

Pandas

  • Introduction to Pandas
  • Series
  • Series indexing and Selection
  • Series Operation
  • Introduction to Pandas
  • Data Frames
  • Data Collection from csv,json,html,excel
  • Data Merging,Concatenation,join
  • Group By and Aggregate Function
  • Order By
  • Missing Value Treatment
  • Outlier Detection and Removal
  • Pandas builtin Data Visualisation
  • HANDS ON
  • INTERVIEW QUESTION DISCUSSION

Module - 3 (Visualisation)

Visualisationmatplotlib, Seaborn

  • Line Plots
  • Scatter Plots
  • Pair Plots
  • Histograms
  • Heat Maps
  • Bar Plots
  • Count Plots
  • Factor Plots
  • Box Plots
  • Violin Plots
  • Swarm Plots
  • Strip Plots
  • Pandas Builtin Visualisation Library
  • HANDS ON
  • INTERVIEW QUESTION DISCUSSION

Module - 4 (Statistics)

Statistics

  • Descriptive vs Inferential Statistics
  • Mean,Median,Mode,Variance,Std. dev
  • Central Limit Theorm
  • Co-Variance
  • Pearson’s Product Moment Correlation
  • R - Square
  • Adjusted R-Square
  • Spearman’s. Rank order Coefficient
  • Sample vs Population
  • Standardizing Data(Z-score)
  • Hypothesis Testing
  • Normal Distribution
  • Bias Variance Tradeoff
  • Skewness
  • P Value
  • Z-test vs T-test
  • The F distribution
  • The chi-Square test of Independence
  • Type-1 and Type-2 errors
  • Annova
  • HANDS ON
  • INTERVIEW QUESTION DISCUSSION

Module - 5 (Intro to ML)

Introduction to Machine Learning

  • Introduction to Machine Leaning
  •  Machine Learning Usecases
  •  Supervised vs Unsupervised vs Semi-Supervised
  •  Machine Learning process Workflow
  •  Training a model
  •  Validating results
  •  Overfitting vs Underfitting
  •  Ordinal vs Nominal data
  •  Structured vs unstructured vs semistructured data
  •  Intro to scikitLearn
  •  HANDS ON

Module - 6 (Supervised)

Regression

  • Regression Vs Classification
  •  Linear regression
  •  Multivariate regression
  •  Polynomial regression
  •  Multi-Colinearity,
  •  Auto correlation
  •  Heteroscedascity
  •  Hands On

Classification

  • KNN
  •  Svm
  •  Decision Tree
  •  Random Forest
  •  Performance tuning of Random Forest
  •  Naive Bayse
  •  Overfitting Vs Underfitting
  •  Hands On

Model Validation

  • Classification Report
  •  Confusion Report
  •  ROC
  •  RMSE
  •  MSE
  •  Cross validation
  •  Hands On

Module - 7 (Unsupervised)

Clustering & PCA

  • Kmeans
  •  How to choose number of K in KMeans
  •  Hands on
  •  PCA
  •  Hands on

Module - 8 (Ensemble)

Ensemble Methods

  • What is Ensembling
  •  Types of Ensembling
  •  Bagging
  •  Boosting
  •  Stacking
  •  Random Forest
  •  Important Feature Extraction
  •  XGBoost
  •  HANDS ON

Module - 9 (NLP)

NLP

  • Tokenizer
  •  Stop Word Removal
  •  Tf-idf
  •  Document similarity
  •  Word2vec Model
  •  t-SNE visualisation
  •  Sentiment Analysis
  •  HANDS ON

Module - 10 (Deep Learning)

Deep Learning

  • Basic of Neural Network
  • Type of NN
  • Cost Function
  • Tensorflow Basics
  • Hands on Simple NN with Tensorflow
  • Image classification using CNN
  • HANDS ON