IQ Stream Technologies in Bangalore offers top quality Data Science training in Bangalore with Placements. IQST, inventateq, besant tech etc are offering top authority data science courses with advanced learning materials and great class room ambience.
Course URL: http://www.iqstreamtech.com/datascience-python-machinelearning.php

Course overview:
Maths – Maths in Data Science include Linear Algebra which refers to familiarity with integrals, differentiations, differential equations, etc.; Statistics including Inferential Statics, Descriptive Statistics, Chi-Squared Tests, Random Variable, Gaussian and Normal Distributions, etc.; Probability like Bayes Theorem, Optimization like Convex Optimization, etc.
Programming Language- Mastering a programming language is highly necessary to pursue Data Science. One can choose from an array of languages including R, Python, Julia, C++, Go, Octave GNU, Matlab, etc. For Python, libraries and packages like Matplotlib, Numpy, Pandas, Seaborn, Scikit-Learn, Scipy, BeautifulSoup, Bokeh, Urllib, PandaSQL, etc., are extremely important to know. Similarly, for R – ggplot2, tidy, etc., are extremely important.
Data Wrangling and Management: This involves thorough understanding of Data Mining, Data Cleaning, Data Management which includes having knowledge of SQL such as MySQL, NoSQL like MongoDB, Cassandra, etc.
Data Analysis and Visualization: Analytics and Visualization forms an integral part of the Data Science workflow. One needs to have the knowledge of using plotting libraries in programming languages like plotly, matplotlib, seaborn in Python and ggplot2 in R. Also having the understanding of using Tableau, Excel and D3.js is considered necessary for interactive visualization.
Machine Learning: Under Machine Learning, topics to be covered are Supervised Learning, Unsupervised Learning, Reinforcement Learning, Testing, Evaluation and Validation of Models to say in brief.

More concisely, the detailed topics are as follows, Linear Regression, Polynomial Regression, Logistic Regression, Decision Trees, Random Forests, Boosted Trees, Naïve Bayes, Bayes Theorem, Ensemble Learning, Ada Boost, Hierarchial Clustering, Divisive and Agglomerative Clustering, DBSCAN, Kmeans Clustering, K-Nearest Neighbours, Perceptrons, Gradient Descent, Multi-Layered Perceptrons(MLP), L1 and L2 Regularization, Cross Validation, Entropy, Train-test, F1 Score, Accuracy, Precision, Recall, Support Vector Machines, Collaborative Filtering, Confusion Matrix, Principal Component Analysis, Dimensionality Reduction and Neural Networks.
Deep Learning- Under Deep Learning, the topics to be covered are- Neural Networks, Feed Forward Neural Networks, Fuzzy Logic, Hyperparamaters Optimization, Sequence Models, Long Short Term Memory (LSTM), Recurrent Neural Nets(RNN), Convolutional Neural Nets(CNN), YOLO, Object Detection, Natural Language Processing, Computer Vision using OpenCV, etc.
BigData – Topics under BigData include MapReduce, Hadoop, Apache, Spark, Hive, Pig, Mahout, Yarn, BigData Analytics, etc.

Statistics with R

Descriptive:

-Data collection

-Data collection - Questionaire Designing

-Data collection - Observation

-Data collection - Case Study Method

-Qualitative Data Vs Quantitative Data

-Data Patterns

-Deciles Statistics

-Venn Diagram

-Central limit theorem

-Chebyshev's Theorem

-Kurtosis

-Normal Distribution

-Laplace Distribution

-Log Gamma Distribution

-Rayleigh Distribution

-Exponential distribution

-Multinomial Distribution

-Binomial Distribution

-Beta Distribution

-F distribution

-Negative Binomial Distribution

-Gamma Distribution

-Chi-squared Distribution

-Geometric Mean

-Harmonic Mean

-Outlier Function

-Stem and Leaf Plot

-Poisson Distribution

-Cumulative Poisson Distribution

-Inverse Gamma Distribution

-Continuous Uniform Distribution

-Hypergeometric Distribution

-Harmonic Number

-Gumbel Distribution

-Comparing plots

-Power Calculator

-Process Sigma

-Harmonic Resonance Frequency

-Standard normal table

-Pooled Variance (r)

-Mean Deviation

-Means Difference

Rcode(+theory) for Descriptive:

central tendency:

-Arithmetic Mean

-Arithmetic Median

-Arithmetic Mode

-Arithmetic Range

-Range Rule of Thumb

-Adjusted R-Squared

-Standard Deviation

-Relative Standard Deviation

-Analysis of Variance

-Grand Mean

-Boxplots

-Quartile Deviation

-Frequency Distribution

-Bar Graph

-Dot Plot

-Scatterplots

-Correlation Co-efficient

-Pie Chart

-Histograms

-Cumulative Frequency

-Cumulative plots

-Skewness

-Goodness of Fit

-Transformations

-Trimmed Mean

-Reliability Coefficient

-Linear regression

-Logistic Regression

-Quadratic Regression

-Regression Intercept Confidence Interval

-Residual sum of squares

-Equation Sum of Square

-Standard Error ( SE )

-Root Mean Square

-Cohen's kappa coefficient

-Ti 83 Exponential Regression

-Shannon Wiener Diversity Index