Best Data Science course in Lucknow - Scimox Data Science Institute

Data Science Training in Lucknow

Scimox provide Data science course in lucknow.Call Now:+ 919457384665


About Data Science with Python Training

ScimoX provides the best industry-relevant content with systematic delivery on the most popular data structure course, Data Science. Python is powerful, easy to learn and flexible tool for coding Data Science and Machine Learning algorithms. In recent years, Python has evolved immensely with respect to Data Science sphere, with a huge community around Python creating quite a few power data science and analytics packages such as Pandas, Numpy, Scikit Learn, Scipy and more. As a result, analyzing data, modeling machine learning algorithms with Python has never been easier. So, Scimox Come with high valued content on Data Science that helps you make your career as a Data Analyst and Data Scientist. We provide domain expert who practices you in all advance library and Live Hands-On Project


Data Science Course Objective

The learning objectives of Data Science in ScimoX :
1.Internalize the concepts & constructs of Data Science
2.Learn to work on Data exploration, data mugging, data pre-processing and transforming Data for further analysis.
3.Master In Data Collection
4.Master in Jupyter & Pycharm IDLE
3. Understand core concepts of Data Science..
4.Learn to Work on Data Collection,Processing data,Exploring and visualizing data and applying machine learning (to data) Deciding (or planning) based on acquired insight
5.100% Interview Guarantee
6.After Placement Career Mentoring



Data Science Course Syllabus


Basic
Python language introduction, Python 3 basics,Keywords in Python, Namespaces and Scope in Python, Structuring Python Programs, Decision making

Input/Output
Taking input in Python, Vulnerability in input() function, Python | end parameter in print(), Python | sep parameter in print()

Data Types
Introduction to DataTypes, Strings,List, Tuples,Sets, Dictionary,

Variables
Variables expression condition and function, Maximum possible value of an integer in python, Global and local variables in python, Packing and unpacking arguments in python, End parameter in Python

Control Flow
Loops , Loops and Control Statements (continue, break and pass) in Python, Looping technique in python, range vs xrange on python, Programs for printing pyramid technique in python Chaining comparison in python, else with for switch function, Using iteration in python effectively Iterators in Python, Iterators function in python Iterators function Python __iter__() and __next__() | Converting an object into an iterator

Modules and Packages:
Modules ,Importing module, Standard Module – sys, Standard Module – OS, The dir Function, Package Exercise

Exception Handling:
Errors, Run Time Errors, Handling IO Exceptions, Try.... except statement, Raise, Assert, Exercise

Files and Directories: Introduction, Writing Data to a File,Reading Data From a File, Additional File Methods, Working with files, Working with Directories, The pickle Module, Exercise

Classes Objects:
Introduction classes and objects, Creating Classes, Instance Methods, Special class method, Inheritance, Method overriding , Data handling Exercise

Getting started with pandas
Data Frame Basics, Key Operations on Data Frames. function, Matplotlib

Plotting for exploratory data analysis (EDA)
Data-point, vector, observation, Input variables/features/dimensions/independent variable , Output Variable/Class Label/ Response Label/ , Scatter-plot: 2D, 3D.

Advance Library:
Multi threading ,Thread, Starting a thread , Threading module, array , numpy ,Tkinter programming Tkinter widget

Probability and Statistics
Introduction to Probability and Stats, Gaussian/Normal Distribution, Uniform Distribution and random number generators Bernoulli and Binomial distribution, Log-normal and power law distribution: Correlation , Confidence Intervals

Linear Algebra
Point/Vector (2-D, 3-D, n-D) ,Dot product and angle between 2 vectors,Projection, unit vector , Equation of a line (2-D), plane(3-D) and hyperplane (n-D) , Square, Rectangle, Hyper-cube and Hyper-cuboid..

Dimensionality reduction and Visualization:
What is dimensionality reduction? , Data representation and pre-processing , MNIST dataset (784 dimensional) , Principal Component Analysis, T-distributed stochastic neighborhood embedding (t-SNE)

Dimensionality reduction and Visualization:
Dataset overview: Amazon Fine Food reviews , Data Cleaning: Deduplication., Featurizations: convert text to numeric vectors, Code samples, Exercise: t-SNE visualization of Amazon reviews with polarity based color-coding

Classification and Regression Models: K-Nearest Neighbors
Foundations, K-Nearest Neighbors , K-Nearest Neighbors ,
Classification algorithms in various situations:
Imbalanced vs balanced dataset, Multi-class classification, k-NN, given a distance or similarity matrix, Train and test set differences, Handling categorical and numerical features.

Performance measurement of models:
Accuracy, Confusion matrix, TPR , FPR , FNR, TNR , Median absolute deviation (MAD) ,Receiver Operating Characteristic Curve (ROC) curve and AUC.

Naive Bayes:
Conditional probability, Independent vs Mutually exclusive events,Bayes Theorem with examples, Toy example: Train and test stages.

Logistic Regression
Geometric intuition, Sigmoid function & Squashing , Optimization problem, Hyperparameter search: Grid Search and Random Search , Column Standardization, Collinearity of features, Train & Run time space and time complexity.

Linear Regression and Optimization
Geometric intuition, Mathematical formulation, Cases

Support Vector Machines (SVM)
Geometric intuition, Mathematical derivation, Loss minimization: Hinge Loss, Kernel trick, Polynomial kernel,RBF-Kernel, Domain specific Kernels.

Decision Trees
Geometric Intuition: Axis parallel hyperplanes,Sample Decision tree, Building a decision Tree, Overfitting and Underfitting,Train and Run time complexity, Regression using Decision Trees,Cases, Exercise: Decision Trees on Amazon reviews dataset

Ensemble Models:
What are ensembles?, Bootstrapped Aggregation (Bagging) , Boosting,Stacking models, Cascading classifiers, Kaggle competitions vs Real world

Featurizations and Feature engineering
Time-series data,mage data, Relational data,Graph data, Feature Engineering,Model specific featurizations, Feature orthogonality

Unsupervised learning/Clustering: K-Means (2)
What is Clustering? ,Unsupervised learning,Applications., Metrics for Clustering,K-Means,

Hierarchical clustering
Agglomerative & Divisive, Dendrograms , Proximity methods: Advantages and Limitations, Time and Space Complexity,Code sample, Exercise: Amazon food reviews

DBSCAN (Density based clustering)
Density based clustering ,MinPts and Eps: Density,Core, Border and Noise points, Density edge and Density connected points,DBSCAN Algorithm

Recommender Systems and Matrix Factorization
Problem formulation: Movie reviews, Content based vs Collaborative Filtering, Similarity based Algorithms,Matrix Factorization,Hyperparameter tuning, Matrix Factorization for recommender systems: Netflix Prize Solution, Cold Start problem, Word Vectors using MF,Eigen-Faces.

Deep Learning: Neural Networks
History of Neural networks and Deep Learning., How Biological Neurons work?, Diagrammatic representation: Logistic Regression and Perceptron,Multi-Layered Perceptron ,Training a single-neuron model,Training an MLP: Chain rule

Deep Learning: Deep Multi-layer perceptrons
1980s to 2010s , Dropout layers & Regularization,Rectified Linear Units (ReLU), Weight initialization,Batch Normalization, Gradient monitoring and Clipping,Softmax for multi-class classification, How to train a Deep MLP? , Auto Encoders

Deep Learning: Tensorflow and Keras.
Overview, GPU vs CPU for Deep Learning , Google Colaboratory ,TensorFlow, MNIST classification in Keras ,Hyperparameter tuning in Keras

Deep Learning: Convolutional Neural Nets
Biological inspiration: Visual Cortex , Convolution,Convolutional layer,Max-pooling,CNN Training: Optimization, ImageNet dataset , Data Augmentation, Convolution Layers in Keras ,AlexNet ,VGGNet ,Residual Network

Deep Learning:Recurrent Neural Networks
Why RNNs,Recurrent Neural Network, Training RNNs: Backprop,Types of RNNs,Need for LSTM/GRU,GRUs,LSTM, Deep RNN,Bidirectional RNN


Fee Structure & Course Duration

• Course :- Data Science
• Level: Master Certification
• Batch: Weekends, Weekdays
• Format: Class Room Training
• Course Fee :- Rs.35000 /-
• Course Duration : 3 Month
• Training Location:Hazaratnagar Branch, Lucknow



Contact Person

HR Counsellor

Mr. Deepak Kanuajia
Email :- Deepak@scimox.com
Contact No. :- (+91) - 8181818821


Benefits of Scimox Training Institute

1.3 Month days Month Complete Data Science Course With Live Project
2.Syllabus is based on Job Description given by Company
3.Resume Modification As Per Company Norm.
4.Working on Live Project
5.1 Year Membership.