# Data Science Training in Chennai

Join the Data Science Training Institute in Chennai – Softlogic Academy. Our training course will give you the required skills to be one of the best picks by the IT employers. Reach the peak of success with the career-oriented approach of SLA!

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## About Data Science Training

In the earlier days, the data that we possessed was not big in size. Moreover, it was structured and simple. Business Intelligence tools could do the work of analyzing. Nowadays the scenario has changed drastically; there is the presence of unstructured or semi-structured nature in most of the data. Simple BI tools cannot analyze such big data. So there arises the need for advanced evaluation tools and rules for gauging and bringing out meaningful ideas from data. Hidden patterns are found out from the raw data with the help of data science. **Data Science Training in Chennai **provides expert practical training in data science.

Interactive Training

The Training sessions in SLA are highly interactive with dedicated attention to each individual.

Online Training

Corporate Training and Customized online Training In SLA Institute for the working professionals.

Practical Training

SLA Focus on not only theory but also practicals so that the student can gain real-time knowledge

## Scope of Data Science Training

**Best Data Science Training Courses in Chennai **assists students in solving difficult analytic problems. Being a blend of technology, data interference and algorithm, data science assists the users to produce huge business value. When the student imbibes the concept of data science, he/she tries to comprehend data patterns and explore details and findings from specific data sets.

By means of the training program at SLA, there is assurance that the students are well-trained and possess adequate specialization in mathematics and statistics. Moreover, the candidate will gain amazing business knowledge. Both students and working professionals can gain from the teaching at SLA.

### Significance of Data Science

Processes and systems form the core of data science. Being an interdisciplinary field, data science’s vastness lies in encompassing several theories and concepts. It relies on information science, statistics, mathematics, computer science etc. There is development of a great deal of data, and several aspects of data science are gaining lot of significance especially big data. **Data Science Courses in Chennai **understand the importance of data science and provides comprehensive training to the aspiring candidate.

When we trace the history of data science, it has turned out to be an essential part of several industries including agriculture, fraud detection, and even public policy. Data science uses an amalgamation of statistics, predictive modeling and machine learning and takes care of several issues in both individual sectors and the economy. **Data Science Training Institute in Chennai **understands that data science is a complex field and hence tries to impart the pertinent knowledge to the student in a profound manner.

The increasing need for better information has transformed the conventional landscape of data science. Machine learning algorithms are also turning out to be the most sought after things by data scientists. Data science and big data are extremely essential areas that are turning out to be very critical. Nowadays the world is collecting data in a speed as never seen before. Moreover, the varied nature and the number of data are growing at an astonishing rate.

This is a data-dependent world;organizations are applying the insights that data scientists give to shine in the crowd. You may question why data scientist is one of the most demanding position nowadays. To put it in a nutshell, there has been a huge burst in both the data produced and retained by organizations. We prefer to form something with a stack of data. Data scientists are those who make sense out of the huge set of data and make out what can be performed with it.

It is turning out vivid now that there is huge value in data processing and evaluation. Here comes the role of the data scientist. Data scientists are involved in the major role of enhancing business outcomes dependent on initial evaluation.

## Our Data Science Training Syllabus

Getting Started

- Course Introduction
- Course Material & Lab Setup
- Installation
- Python Basic – Part – 1
- Python Basic – Part – 2
- Advance Python – Part – 1
- Advance Python – Part – 2

#### Statistics and Probability Refresher, and Python Practice

- Types of Data
- Mean, Median, Mode
- Using mean, median, and mode in Python
- Variation and Standard Deviation
- Probability Density Function; Probability Mass Function
- Common Data Distributions
- Percentiles and Moments
- A Crash Course in matplotlib
- Covariance and Correlation
- Conditional Probability
- Exercise Solution: Conditional Probability of Purchase by Age
- Bayes’ Theorem

#### Predictive Models

- Linear Regression
- Polynomial Regression
- Multivariate Regression, and Predicting Car Prices
- Multi-Level Models

#### Machine Learning with Python

- Supervised vs. Unsupervised Learning, and Train/Test
- Using Train/Test to Prevent Overfitting a Polynomial Regression
- Bayesian Methods: Concepts
- Implementing a Spam Classifier with Naive Bayes
- K-Means Clustering
- Clustering people based on income and age
- Measuring Entropy
- Install GraphViz32. Decision Trees: Concepts
- Decision Trees: Predicting Hiring Decisions
- Ensemble Learning
- Support Vector Machines (SVM) Overview
- Using SVM to cluster people using scikit-learn

#### Recommender Systems

- User-Based Collaborative Filtering
- Item-Based Collaborative Filtering
- Finding Movie Similarities
- Improving the Results of Movie Similarities
- Making Movie Recommendations to People
- Improve the recommender’s results

#### More Data Mining and Machine Learning Techniques

- K-Nearest-Neighbors: Concepts
- Using KNN to predict a rating for a movie
- Dimensionality Reduction; Principal Component Analysis
- PCA Example with the Iris data set
- Data Warehousing Overview: ETL and ELT
- Reinforcement Learning

#### Dealing with Real-World Data

- Bias/Variance Tradeoff
- K-Fold Cross-Validation to avoid overfitting
- Data Cleaning and Normalization
- Cleaning web log data
- Normalizing numerical data
- Detecting outliers

#### Apache Spark: Machine Learning on Big Data

- Lab Set-up Warning & Error Handling
- Installing Spark – Part – 1
- Installing Spark – Part – 2
- Spark Introduction
- Spark and the Resilient Distributed Dataset (RDD)
- Introducing MLLib
- Decision Trees in Spark
- K-Means Clustering in Spark
- TF / IDF
- Searching Wikipedia with Spark
- Using the Spark 2.0 DataFrame API for MLLib

#### Experimental Design

- A/B Testing Concepts
- T-Tests and P-Values
- Hands-on With T-Tests
- Determining How Long to Run an Experiment
- A/B Test Gotchas

#### Deep Learning and Neural Networks

- Deep Learning Pre-Requisites
- The History of Artificial Neural Networks
- Deep Learning in the Tensorflow Playground
- Deep Learning Details
- Introducing Tensorflow
- Using Tensorflow, Part 1
- Using Tensorflow, Part 2
- Introducing Keras
- Using Keras to Predict Political Affiliations
- Convolutional Neural Networks (CNN’s)
- Using CNN’s for handwriting recognition
- Recurrent Neural Networks (RNN’s)
- Using a RNN for sentiment analysis
- The Ethics of Deep Learning
- Learning More about Deep Learning

#### Statistics and Data Science in R

#### Introduction

- Introduction to R
- R and R studio Installation & Lab Setup
- Descriptive Statistics

#### Descriptive Statistics

- Mean, Median, Mode
- Our first foray into R : Frequency Distributions
- Draw your first plot : A Histogram
- Computing Mean, Median, Mode in R
- What is IQR (Inter-quartile Range)?
- Box and Whisker Plots
- The Standard Deviation
- Computing IQR and Standard Deviation in R

#### Inferential Statistics

- Drawing inferences from data
- Random Variables are ubiquitous
- The Normal Probability Distribution
- Sampling is like fishing
- Sample Statistics and Sampling Distributions

#### Case studies in Inferential Statistics

- Case Study 1 : Football Players (Estimating Population Mean from a Sample)
- Case Study 2 : Election Polling (Estimating Population Proportion from a Sample)
- Case Study 3 : A Medical Study (Hypothesis Test for the Population Mean)
- Case Study 4 : Employee Behavior (Hypothesis Test for the Population Proportion)
- Case Study 5: A/B Testing (Comparing the means of two populations)
- Case Study 6: Customer Analysis (Comparing the proportions of 2 populations)

#### Diving into R

- Harnessing the power of R
- Assigning Variables
- Printing an output
- Numbers are of type numeric
- Characters and Dates
- Logicals

#### Vectors

- Data Structures are the building blocks of R
- Creating a Vector
- The Mode of a Vector
- Vectors are Atomic
- Doing something with each element of a Vector
- Aggregating Vectors
- Operations between vectors of the same length
- Operations between vectors of different length
- Generating Sequences
- Using conditions with Vectors
- Find the lengths of multiple strings using Vectors
- Generate a complex sequence (using recycling)
- Vector Indexing (using numbers)
- Vector Indexing (using conditions)
- Vector Indexing (using names)

#### Arrays

- Creating an Array
- Indexing an Array
- Operations between 2 Arrays
- Operations between an Array and a Vector
- Outer Products

#### Matrices

- A Matrix is a 2-Dimensional Array
- Creating a Matrix
- Matrix Multiplication
- Merging Matrices
- Solving a set of linear equations

#### Factors

- What is a factor?
- Find the distinct values in a dataset (using factors)
- Replace the levels of a factor
- Aggregate factors with table()
- Aggregate factors with tapply()

#### Lists and Data Frames

- Introducing Lists
- Introducing Data Frames
- Reading Data from files
- Indexing a Data Frame
- Aggregating and Sorting a Data Frame
- Merging Data Frames

#### Regression quantifies relationships between variables

- Linear Regression in Excel : Preparing the data.
- Linear Regression in Excel : Using LINEST()

#### Linear Regression in R

- Linear Regression in R : Preparing the data
- Linear Regression in R : lm() and summary()
- Multiple Linear Regression
- Adding Categorical Variables to a linear mode
- Robust Regression in R : rlm()
- Parsing Regression Diagnostic Plots

#### Data Visualization in R

- Data Visualization
- The plot() function in R
- Control color palettes with RColorbrewer
- Drawing bar plots
- Drawing a heatmap
- Drawing a Scatterplot Matrix
- Plot a line chart with ggplot

### Why SLA for Data Science Training

SLA offers dedicated training course for data science certification course. Placement assistance is one of its forte. We ascertain that the aspiring candidates gain the needed experience and competence coupled with thorough subject knowledge. **Data Science Training Course in Chennai **consists of the best industry experts who train the students to be highly competent.

The aspiring candidate who is a working professional and who is on the lookout for flexible timings can reach our student center for the purpose of corporate training or personalized online training. Practical training is one of the specialties of our teaching. The course materials are adequate and the students are well prepared for the interviews. Besides technical skills, soft skills are also concentrated. Both beginners and experienced professionals can gain from the in-depth training offered by SLA. **Data Science Certification Training Course in Chennai **has a positive impact on the knowledge of the student.

##### Data Science Training in Chennai SLA – Certification

A course completion certificate is a proof of your knowledge and would scale up your career. On completion of the data science training, the student will receive a course completion certificate.

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