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Tag: data analytics

Statistics For Data science

Posted on by Imarticus Learning

Data Science is the effective extraction of insights and data information. It is the science of going beyond numbers to find real-world applications and meanings in the data. To extract the information embedded in complex datasets, Data Scientists use myriad techniques and tools in modelling, data exploration, and visualization.

The most important mathematical tool of statistics brings in a variety of validated tools for such data exploration. Statistics is an application of mathematics that provides for mathematical concrete data summarization. Rather than use one or all data points, it renders a data point that can be effectively used to describe the properties of the point regarding its make-up, structure and so on.

Here are the most basic techniques of statistics most popularly used and very effective in Data Science and its practical applications.

(1) Central Tendency

This feature is the typical variable value of the dataset. When a normal distribution is x-y centered at (110, 110) it means the distribution contains the typical central tendency (110, 110) and that this value is chosen as the typical summarizing value of the data set. This also provides us with the biasing information of the set.

There are 2 methods commonly used to select central tendency.

Mean:

The average value is the mid-point around which data is distributed. Given 5 numbers here is how you calculate the Mean. Ex: There are five numbers

Mean= (188 2 63 13 52) / 5 = 65.6 aka mathematical average value used in Numpy and other Python libraries.

Median:

Median is the true middle value of the dataset when it is sorted and may not be equal to the mean value. The Median for the sample set requires sorting and is:

[2, 13, 52, 63, 188] → 52

The median and mean can be calculated using simple numpy Python one-liners:

numpy.median(array)

numpy.mean(array)

(2) Spread

The spread of data shows whether the data is around a single value or spread out across a range. If we treat the distributions as a Gaussian probability figure of a real-world dataset, the blue curve has a small spread with data points close to a narrow range. The red line curve has the largest spread. The figure also shows the curves SD-standard deviation values.

Standard Deviation:

This quantifies the spread of data and involves these 5 steps:

1. Calculate mean.

2. For each value calculate the square of its distance from the mean value.

3. Add all the values from Step 2.

4. Divide by the number of data points.

5. Calculate the square root.

Made with https://www.mathcha.io/editor

Bigger values indicate greater spread. Smaller values mean the data is concentrated around mean value.

In Numpy SD is calculated as

numpy.std(array)

(3) Percentiles

The percentile shows the exact data point position in the range of values and if it is low or high.

By saying the pth percentile one means there is p% of data in the lower part and the remaining in the upper part of the range.

Take the set of 11 numbers below and arrange them in ascending values.

3, 1, 5, 9, 7, 11, 15,13, 19, 17, 21. Here 15 is at the 70th percentile dividing the set at this number. 70% lies below 15 and the rest above it.

The 50th percentile in Numpy is calculated as

numpy.percentile(array, 50)

(4) Skewness

The Skewness or data asymmetry with a positive value means the values are to the left and concentrated while negative means a right concentration of the data points.

Skewness is calculated as

Skewness informs us about data distribution is Gaussian. The higher the skewness, the further away from being a Gaussian distribution the dataset is.

Here’s how we can compute the Skewness in Scipy code:

scipy.stats.skew(array)

(5) Covariance and Correlation

Covariance

The covariance indicates if the two variables are “related” or not. The positive covariance means if one value increases so do the other and a negative covariance means when one increases the other decreases.

Correlation

Correlation values lie between -1 and 1 and are calculated as the covariance divided by the product of SD of the two variables. When 1 it has perfect values and one increase leads to the other moving in the same direction. When less than one and negative the increase in one leads to a decline in the other.

Conclusion: 

When doing PCA-Principal Component Analysis knowing the above 5 concepts is useful and can explain data effectively and helps summarize the dataset in terms like correlation in techniques like Dimensionality Reduction. Thus when more data can be defined by a median or mean values the remaining data can be ignored. If you want to learn data science, try the Imarticus Learning Academy where careers in data science are made.

Should You Start With Big Data Training or Learn Data Analytics First?

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Should you start with big data training or learn data analytics? Which one should I start first?

We live in a highly interconnected and dependent world of technology wherein the amount of technology we use is like a drop in the ocean. At the pace we are traveling in this digital world approximately 2.5 quintillion bytes of data is generated on a daily basis. Yes, it is indeed a staggering amount of data that is being produced.

So, the application of analytics in Big Data has various merits for businesses. Hence, businesses look forward to using this to gain a competitive edge over their competitors in the foreseen future. Though businesses understand the prominence of big data they are unaware of using big data to achieve the desired success.

So, ideally, there is a huge scope for talented who are capable of stimulating the business in the route of success using big data. For data science aspirants it is a wise choice to start with big data training than Data Analytics Course. Read on to know more about it!!

Difference between big data and data analytics

The primary difference between the two is that big data is centered around figuring out meaningful insights within a large pile-up of either structured or unstructured data whereas data analytics is more focused and looks out through relevant data to solve business problems.  Big data training consists of complex skills which will be a great addition on top of your knowledge in statistics, database topics, and programming languages. You can also see that most companies are dependant on Hadoop training which essentially helps you to assimilate the huge data using programming languages like Java, C, Python, Swift, etc.

On the other end, Data Analytics Training on operational insights of the business by making predictive models using programming languages and uses manipulative techniques for understanding the trends. Understanding historical data and extracts interferences from it to solve complex business issues.

Tools and skill sets that differentiate the two courses

Typically having good insights about databases, programming languages, frameworks like Apache and Hadoop and coding would help you positively for big data training. Basic knowledge of statistics and mathematics is essential along with creativity to filter a large database. Knowledge about statistics and mathematics along with data wrangling is required to become an expert in data analytics.  Big data utilizes complex technological tools whereas data analytics uses statistical and straightforward tools.

Various tools like Hadoop, Tableau, NoSQL, R and many more are used to draw interference from big data to get desirable graphics, statistical data, and visualization. Learning R programming language is essential to learn Data Analytics due to its widespread use of tools to deal with statistical and analytical data. So, R developers have an edge over others in learning data analytics. Whereas Big data efficiently uses MapReduce, a programming model for processing huge amount of data.  When MapReduce is coupled with Hadoop Distributed File System (HDFS) for its efficient use in Big Data.

What should you do to master big data?

In the bustling world of digital technology, we have access to any information presented by experts in the field of data. Enrolling in either big data training or data analytics courses will definitely be useful to fill the gap between the demand and supply in big data. Having a diversified skill set will give you an edge over your competitors. Big data training and data analytics classes are available online in reputed institutes who provide hands-on training to understand and interpret the concepts in real-time business situations. Look out for training institutes who provide comprehensive insights and training in big data for gaining proficiency in the subject right now.

Conclusion

With an acute shortage of skilled in the field of big data, its demand is set to increase and is deemed to be a long-term growth-oriented career option. As you can understand one course is used to manage large sets of a database, on the other hand, another uses such a database to gain meaningful insights. Learning Big data training may be a smart option for landing in your dream job, it is your call to take up either of the courses first. Take a step forward right now to taste success in the near future!!!

For more details in brief and further career counseling, you can also search for – Imarticus Learning and can drop your query by filling up a form from the website or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Banglore, Hyderabad, Delhi,  Gurgaon, and Ahmedabad.

How are Online Retailers Using Big Data Analytics?

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Data is being generated at every moment of the day and has grown from retailers using their own data to databases available across industrial verticals. It is so huge that cloud storage is now the buzz word. Data analytics with the Big tag deals with data primarily and the predictions or forecasts from analyzing databases that help with informed decision making in all processes related to business. This could run into volumes of several petabytes of data.
But, why would one need a Big Data Analytics Course? Because smaller databases that are less than a terabyte size-wise can be tackled with traditional tools. However, modern data tends to be unstructured and comes in the form of videos, audio clips, blog posts, reviews, and more which are challenging to clean, organize and include huge volumes of data.
The tools and techniques involved in the capture, storage, and cleaning of data need necessarily to be updated. One also would need faster software that can compare databases across platforms, operating systems, programming languages and such complexities of technology.
The speed and agility of analytics offer big advantages and savings in making informed business decisions. That’s why investing in data analytics and Data Analytics Training is such a popular choice across industrial verticals and sectors.
Let us look at the data analytics improvements of some real-life examples.

Offering marketing insights:

Foresight from analytics has the potential to change marketing strategy, operations and more in all firms. Whether it be effective marketing strategy or promotional campaigns, decision making, purchasing, cost-saving measures, targeting the customers, promoting products or improving efficiency through the predictions, insights, forecasts, etc help make those decisions. Just look at the campaign of Netflix covering over 100 million customers for inspiration.

Boosting retention and Customer-Acquisition:

Coca Cola used their data foresight to draw up their retention and loyalty reward programs and to improve their services, products, and customer stories. Besides boosting sales such improvements trigger loyalty too.

Regulatory compliance and Risk Management insights:

Singapore based UOB did their risk assessment and management for the financial sector and budgeting. Foresight and predictions can also be effectively used as a critical investment in regulatory compliance.

Product innovations:

Take the example of Amazon’s diversification into groceries, food, and fresh-foods segment. Their analytics program was based on the acceptance of customers trends and successfully helped innovate product lines, design models of innovation in saleable products, etc.

Management of logistics and supply-chains:

This essential field can be transformed very effectively as Pepsico did with improved processes, scheduling deliveries, warehouse management, reconciling logistics and shipment needs and more.
Budget and spending predictions:
The loyalty of customers is reflected in spending patterns and data is collected from use of credit cards, effects of promotional programs and customer retention data, web users log-in data, IP addresses, etc to gauge predictions for spending and effective budgeting. Did you know that Amazon analyses accounts that run into astounding figures like 150 Mil customers and their analytics programs increased sales by 29 percent and new customers by 40 percent? That’s huge profits from data analytics!

Bettering customer service:

Improvement in customer experience yields big dividends as in the case of Costco where specific customers who were at risk with listeria contamination in fruits and were warned instead of creating a scare with emails to all customers.

Demand forecasting:

Just look at the Pantene and Walgreens hair-care products sales figures. They promoted the products based on a demand prediction of weather and anticipated higher humidity affecting sales of anti-frizz hair products. Pantene recorded a 10 % increase and Walgreens a 4% sales increase. Smart use of data analytical predictions by retailers!

Research on journeys of customers:

This graph is never a straight line and when in retail marketing analytics with many thousands of customers, one can help understand data like where an individual customer will seek product info, how and where to reach such customers, why the customer loyalty changed, etc. Looking for the needle in the haystack is now easy with data analytics.

Concluding note:

All enterprises, especially in the retail sector, need big data analytics to have reduced operational expenses, a competitive edge, enhanced customer loyalty, better productivity, and retention. The demand for data analysts keeps growing alongside the growth of data and is an ideal choice of careers with scope, payouts, and growth. If you wish for a Data Analytics career, then do a big data analytics course at the reputed Imarticus Learning. Their data analytics training with assured placement, certification, soft skill modules,industry-suited curriculum, and real-time project work offers the best career choices. Enroll today!

Retail Analytics – How Does It Help Boost The Sales?

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SMB retailers benefit in three main ways from retail data analytics. 

1. Knowing Customers:

Singapore’s Dish-the-Fish fish-stall uses inventory and sales analytics on Vend’s retail management platform and cloud-based POS. Owner Jeffrey Tan prior to switching to the platform, bought what he thought to be the fastest selling fish the ikan kuning. On tracking data by the hour on different fish sales frequencies on Vend’s POS system, he found the leatherjacket fish was fast-selling though pricier. Monitoring in real-time also gave Tan the data-analytics ability to track and cater to the preferences and tastes of his clients. According to data from Accenture, 65% of clients buy from brands that know their brand preferences and buying history.

2. Analyzing Trends:

To use data analytics effectively one must know when and what the customers want even before they produce it. Just look at Dash a fashion store! The store’s Retail Director, Dakota DiSanto, admitted that before switching to LightSpeed’s POS system, her staff spent as many as 8 man-hours per week on studying and tracking manually the sales, inventory, re-order items and so on. According to her the real-time inventory view, sales trends and stock levels across their operational stores in Miami and Los Angeles provided them crucial information on the best sellers, re-ordered units, inventory scheduling, etc well ahead of the demand.

3. The True Costs:

Marquis Gardens’ Ostap Bosak, the General manager, used ACCEO’s POS system. Being Toronto based pond-supply retailers he made use of the transport insights from data analysis on their retail operations. Here’s his story.

On evaluating his data he dropped several suppliers as he found they were earning too little and working too much on them. Though they formed a major portion of the revenue generations the sidelined products were more profitable. He then focused on the main two generators of revenue namely the small pond kit and the pond-less waterfall kit. Bosak stated that he was able to better monitor the ROI from his data analysis as it enabled him to watch over the metric of profit with respect to time spent and efforts spent on it. Bosak reasserts that most businesses do not account for the actual man-hours taken while calculating profitability. In his opinion retail data-analytics helps drill into data in greater minute details to help sustain your operations in a fiercely competitive market.  

Which metrics should one analyze?

Analysis of KPIs like foot-fall traffic, margins, sales growth, and walk-in rates speak the numbers-story of any enterprise with accuracy and transparency to enable your making profitable decisions with those data analytics insights. Here are the metrics in retail business analytics every store must and should monitor.

  1. The square foot rate of sales 
  2. Rate of Retail Conversion 
  3. Net Margins on profit

1. Sales/SqFt:

This index helps. Because, when you know exactly how many sales you earn per sqft of space you can assess and gauge the store’s performance to

  • Refurbish your retail layout: Express rearranged the layout of its store bringing its merchandise selling at full-price to the front and taking the other discounted apparel to the rear-end, based on its analytics and trends in sales. The results showed in a spurt of sales in the more profitable full-price range.
  • Pile up effectively: The fashion boutique Covet’s owner Adrienne Wiley cautions retailers to carefully monitor sales data when they decide on inventories and range of products to sell. She benefited by stocking up the necklaces and tweaking the sales/hour figures in her data analytics analysis.

2. Rate of Retail Conversion:

Browsers are common and this metric gauges how many of them you convert into sales or buyers of your merchandise. So, why study and analyze data when you cannot use it. Right? No, wrong! Here’s what to do with it. 

  • Figure out why customers buy and what keeps them from buying: Your low sales could result from poor displays, long billing times, lack of sales reps or customers not finding what they want. If you take the time to speak to and observe customers respond you can find ways to make their journey more pleasurable and this would result in repeat sales and loyal customers.
  • Set goals and train your employees: Employees are an organizational asset. Train employees to make the customer experience good using goal-setting, loyalty-rewards and incentives. The Friedman Group Founder Harry Friedman claims training helps retail organizations push sales 15-25%.

Wrapping up, there can be no doubt that data analytics enables boosting sales. So, do a course in data analysis with Imarticus Learning. They get you career-ready from day one in a variety of interesting subjects.

Top Python Libraries For Data Science

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Top 10 Python Libraries For Data Science

With the advent of digitization, the business space has been critically revolutionized and with the introduction of data analytics, it has become easier to tap prospects and convert them by understanding their psychology by the insights derived from the same. In today’s scenario, Python language has proven to be the big boon for developers in order to create websites, applications as well as computer games. Also, with its 137000 libraries, it has helped greatly in the world of data analysis where the business platforms ardently require relevant information derived from big data that can prove conducive for critical decision making.

Let us discuss some important names of Python Libraries that can greatly benefit the data analytics space.

Theono

Theono is similar to Tensorflow that helps data scientists in performing multi-dimensional arrays relevant to computing operations. With Theono you can optimize, express and array enabled mathematical operations. It is popular amongst data scientists because of its C code generator that helps in faster evaluation.

NumPy

NumPy is undoubtedly one of the first choices amongst data scientists who are well informed about the technologies and work with data-oriented stuff. It comes with a registered BSD license and it is useful for performing scientific computations. It can also be used as a multi-dimensional container that can treat generic data. If you are at a nascent stage of data science, then it is key for you to have a good comprehension of NumPy in order to process real-world data sets. NumPy is the foundational scientific-computational library in Data Science. Its precompiled numerical and mathematical routines combined with its ability to optimize data-structures make it ideal for computations with complex matrices and data arrays.

Keras

One of the most powerful libraries on the list that allows high-level neural networks APIs for integration is Keras. It was primarily created to help with the growing challenges in complex research, thus helping to compute faster. Keras is one of the best options if you use deep learning libraries in your work. It creates a user-friendly environment to reduce efforts in cognitive load with facile API’s giving the results we want. Keras written in Python is used with building interfaces for Neural Networks. The Keras API is for humans and emphasizes user experience. It is supported at the backend by CNTK, TensorFlow or Theano. It is useful for advanced and research apps because it can use individual stand-alone components like optimizers, neural layers, initialization sequences, cost functions, regularization and activation sequences for newer expressions and combinations.

SciPy

A number of people get confused between SciPy stack and library. SciPy is widely preferred by data scientists, researchers, and developers as it provides statistics, integration, optimization and linear algebra packages for computation. SciPy is a linked library which aids NumPy and makes it applicable to functions like Fourier series and transformation, regression and minimization. SciPy follows the installation of NumPy.

NLKT

NLKT is basically national language tool kit. And as its name suggests, it is very useful for accomplishing national language tasks. With its help, you can perform operations like text tagging, stemming, classifications, regression, tokenization, corpus tree creation, name entities recognition, semantic reasoning, and various other complex AI tasks.

Tensorflow

Tensorflow is an open source library designed by Google that helps in computing data low graphs with empowered machine learning algorithms. It was created to cater to the high demand for training neural networks work. It is known for its high performance and flexible architecture deployment for all GPUs, CPUs, and TPUs. Tensor has a flexible architecture written in C and has features for binding while being deployed on GPUs, CPUs used for deep learning in neural networks. Being a second generation language its enhanced speed, performance and flexibility are excellent.

Bokeh

Bokeh is a visualization library for designing that helps in designing interactive plots. It is developed on Matplotib and supports interactive designs in the web browser.

Plotly

Plotly is one of the most popular and talked about web-based frameworks for data scientists. If you want to employ Plotly in your web-based model is to be employed properly with setting up API keys.

 

SciKit-Learn

SciKit learn is typically used for simple data related and mining work. Licensed under BSD, it is an open source. It is mostly used for classification, regression and clustering manage spam, image recognition, and a lot more. The Scikit-learn module in Python integrates ML algorithms for both unsupervised and supervised medium-scale problems. Its API consistency, performance, documentation, and emphasis are on bringing ML to non-specialists in a ready simple high-level language. It is easy to adapt in production, commercial and academic enterprises because of its interface to the ML algorithms library.

Pandas:

The open-source library of Pandas has the ability to reshape structures in data and label tabular and series data for alignment automatically. It can find and fix missing data, work and save multiple formats of data, and provides labelling of heterogeneous data indexing. It is compatible with NumPy and can be used in various streams like statistics, engineering, social sciences, and finance.

Theano:

Theano is used to define arrays in Data Science which allows optimization, definition, and evaluation of mathematical expressions and differentiation of symbols using GPUs. It is initially difficult to learn and differs from Python libraries running on Fortran and C. Theano can also run on GPUs thereby increasing speed and performance using parallel processing.

PyBrain

PyBrain is one of the best in class ML libraries and it stands for Python Based Reinforcement Learning, Artificial Intelligence. If you are an entry-level data scientist, it will provide you with flexible modules and algorithms for advanced research. PyBrain is stacked with neural network algorithms that can deal with large dimensionality and continuous states. Its flexible algorithms are popular in research and since the algorithms are in the kernel they can be adapted using deep learning neural networks to any real-life tasks using reinforcement learning.

Shogun:

Shogun like the other Python libraries has the best features of semi-supervised, multi-task and large-scale learning, visualization and test frameworks; multi-class classification, one-time classification, regression, pre-processing, structured output learning, and built-in model selection strategies. It can be deployed on most OSs, is written in C and uses multiple kernel learning, testing and even supports binding to other ML libraries.

 

Comprehensively, if you are a budding data analyst or an established data scientist, you can use the above-mentioned tools as per your requirement depending on the kind of work you’re doing. This is why it is very important to understand the various libraries available that can make your work much easier for you to accomplish your task much effectively and faster. Python has been traversing the data universe for a long time with its ever-evolving tools and it is key to know them if you want to make a mark in the data analytics field. For more details, in brief, you can also search for – Imarticus Learning and can drop your query by filling up a simple form from the site or can contact us through the Live Chat Support system or can even visit one of our training centers based in – Mumbai, Thane, Pune, Chennai, Bangalore, Hyderabad, Delhi and Gurgaon.

Big Data Analytics With Hadoop

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Hadoop has been around forever; right from the early days of data analytics and the big data analytics, Hadoop has been an integral part and well-known name in the IT and data analytics industry. Formally known as Apache Hadoop, it is an open source software developed partly in partnership with Apache Software Foundation. 

Today, the software is known across the globe and is used in managing data processing as well as storage for big data applications which run on clustered systems. Hadoop being a well-known name in the data analytics industry is at the center of a dynamic market whose need for big data analytics is constantly increasing. The main factor that contributes to the wide use of Hadoop in data analytics is its ability to handle and manage various applications like predictive analytics, data mining as well as machine learning. 

A feature that distinguishes Hadoop from all other tools available in the market is its ability to handle both structured and unstructured data types, thus giving users increased flexibility for collecting, processing and analyzing big data, which conventional systems like data warehouses and relational databases can’t provide. 

Hadoop and Data Analytics 

As mentioned in the introductory paragraphs, Hadoop is essentially an analytics software for big data and can run on massive clusters of servers, thus providing the user with the ability to support thousands of nodes and humongous amounts of data. Since its inception in the mid-2000s, Hadoop has become an integral part of all data analytics operations mainly because of its significant features like managing nodes in a cluster, fault tolerance capabilities and many more. 

Hadoop due to its wide range of capabilities is a very good fit for any big data analytics application. Due to its capacity to handle any form of data, be it structured or unstructured, Hadoop can handle it all. One of the most notable applications of Hadoop includes its use in customer analytics. With Hadoop, users can predict anything, be it customer churn, analyze click-stream data or analyze and predict the results of an online ad. 

Top Big Data Analytics Tools

Although Hadoop is at the center of big data analytics, there are many notable tools in the market that are definitely worth checking out. Some of the most significant ones are as mentioned below. 

  • R Programming

After Hadoop, R is the leading data analytics tool in the market today. Available in Windows, Mac as well as Linux, R is most commonly used in statistics and data modelling. 

  • Tableau Public

 Tableau Public is an open source, free data analytics tools that have the capability to seamlessly connect data warehouses, Excel or any other source and display all the data on a web-based dashboard with real-time updates. 

  • SAS

SAS is the global leader in data analytics for many years and is widely known for its easy accessibility and manipulation capabilities. 

Conclusion

Hadoop and Big Data Analytics are terms that are synonymous with each other. With Hadoop and the right source, a user can analyze any type of data imaginable. 

Analytics and Agriculture

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Agriculture drives the Indian economy with a whopping population of nearly 70% in rural areas and 40% being part of the agricultural workforce. However, it has many issues and hurdles in realizing its full potential and leveraging analytics and technology for it. The sector lacks banking, financial, disaster management, and water inadequacy facilities and infrastructure. Also due to lack of education migration to cities is a major issue. Though in the early stages the policymakers were quick to realize the potential of analytics and technology in mitigating the hardships of farmers and slowly but steadily the combination is appearing to slow down and address the agriculture segment pressing issues.
Use of Big Data Analytics:
Data is the life breath of all activities in modern times and in agriculture too. Leveraging the potential of analytics and Big Data can bring about immense changes in agriculture and its productivity. The frequent news-releases on droughts, crop failures, farmer suicides and such acute results of backward farming and agriculture stresses the need for the involvement of technology and big data in improving the lot of the farmers and agriculture segment. Be it crop patterns, wind directions, crop loss mitigation, soil adequacy, and fertility, it is Big Data analytics that has offered solutions using technologies like

  • Cloud and Nanocomputing
  • Big data, digitalization and visualization use.
  • AI, IoT and ML use.
  • Saas Platforms, cloud services, and web-based apps.

Role of data and the data analyst:

Agriculture is interdisciplinary and combines concepts of business management, chemistry, mathematics, statistics, physics, economics, and biology. Like all interdisciplinary sectors, the need for data and its use is crucial for growth, change, and development. This means that like in other segments the data analyst role is both well-paying, has an unending scope and relies on a variety of latest futuristic technologies and smart apps.
Knowledge of sciences, agriculture methods, biotechnology, animal and soil sciences, etc will definitely aid the analyst. The analyst will also need proficiency in analysis techniques, data prepping and predictive analysis.
Analytical technologies in the agriculture sector can be used effectively in 

  • Capturing data: using the IoT, biometrics, sensors, genotyping, open and other kinds of data, etc.
  • Storage of Data: using data lakes, Hadoop systems, Clouds, Hybrid files and storage, etc.
  • Transfer of Data: via wireless and wifi, linked free and open source data, cloud-based solutions, etc.
  • Analytics and Transformation of data: through ML algorithms, normalization, computing cognitively, yield models, planting solutions, benchmarks, etc.
  • Marketing of data and its visualization.

What is Smart Farming?

Smart Farming uses analytics, IoT, Big Data and ML to combine technology and agriculture applications. Farming solutions also offer

  • ML and data visualization techniques.
  • App-based integration for data extraction and education.
  • Monitoring through drones and satellites.
  • Cloud storage for securing large volumes of data.

Smart Farming technologies and analytics can thus be efficiently used for forecasts, predictions for better crop harvests, risk mitigation, and management, harvest predictions, maximizing crop quality, liaising and interconnectivity with seed manufacturers, banks, insurers, and government bodies.

What is Precision Agriculture?

This methodology is about Crop Management which is site-specific and also called ‘Farming using Satellites’. The information from satellites helps distill data regarding topography, resources, water availability, the fertility of the soil, nitrogen, moisture and organic matter levels, etc which are accurately measured and observed for a specific location or field. Thus an increase in ROI and optimization of resources is possible through satellite aided analytics. Other devices like drones, image files from satellites, sensors, GPS devices, and many more can prove to be helpful aids and are fast becoming popular.

Concluding with the challenges:

Though the technologies are the best the implementation and applications to the agriculture sector are lacking. Education and training of the farmers is the best solution but involves a lot of man-hours, uninterrupted power, use of data efficiently, internet connectivity, and finance to help these measures succeed and develop to their full potential. Right now it is in the nascent stage and the need for data analysts is very high.  To get the best skill development training courses in data analytics do try Imarticus Learning which is a highly recommended player with efficient, practical skill-oriented training and assured placements as a bonus. Where there is a will the way will show up on its own. Hurry and enroll.

Can you become a Data analyst by online tutorials?

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In an age where tutorials and lectures are heavily sought after both online and offline, it is easy to see why online tutorials are on-demand, especially to those who are already occupied with jobs with heavy schedules and those professionals who experience time constraints to attend an actual full-time offline course. Although the teaching methods, means, and experience of that of an online tutorial may be quite different, if you are a good self-starter and self-learner, it is quite an engaging and educative activity you can invest your time in regularly.
Let us understand how to learn Data Analytics through online tutorials will guarantee you in becoming a Data Analyst professional. Some of these points are discussed below –

  1. Avail Online Big Data Analytics course for a minimum fee– Regular online classes, engaging, recorded lectures and practical projects help you gain great insight and enhance your skills regarding your subject matter. There are various online options for you to register and enroll for a course in Data Analysis. It sometimes has payment requests and you will need to pay the required fee for accessing these classes. To maintain a certain quality and standard some of these courses are priced with a standard fee structure.
  2. A wide variety of knowledge base in Data Analysis to choose from – You can choose from various types of Data Analysis courses that have the online classes option. From the IBM Data Science Professional Certificate to Applied Data Science with Python to Business Analytics to learning the Data Scientist’s Toolbox, the choices for you to pick from are vast and varies, giving you the opportunity to truly specialize and focus on your favorite subject matter.
  3. Globally recognized online courses – Not only do you have the benefit of investing only a small amount for your Data Analysis certification course, but you will also have global validation for the said course(s) This added advantage makes your knowledge base, skills, tools and techniques learned under the course internationally relevant. This naturally means a great score of career options and job opportunities will now be open to you.
  4. Free courses – Sometimes there are courses offered absolutely free of cost. Data Analysis has several such courses offered free of cost. The option of the syllabus may be limited but you will gain a little above the general knowledge of the certification course and will be able to become relevant with the skills and knowledge you achieve through this online engagement.

From the above factors it is evident that through practical application, patience and practice, you can forge into a  professional Data Analyst career with online support and tutorials. If you expand your knowledge base, there are further professional certifications and degrees to be awarded too. This is available online as well. However, the fee and eligibility criteria may vary accordingly.
So, go on, search for that perfect online course or online tutorial and equip yourself in becoming the best Data Analyst you know. With basic know-how, a minimum investment of money and time, practice and consistent efforts, turn your Data Analyst dream into reality!

How is Big Data Analytics Used For Stock Market Trading?

Posted on by Imarticus Learning

How is big data analytics used for stock market trading?

Big Data Analytics is the winning ticket to compete against the giants in the stock market. Data Analytics as a career is highly rewarding monetarily with most industries in the market adopting big data to redefine their strategies. Online stock market trading is certainly one area in the finance domain that uses analytical strategies for competitive advantage. 

Capital market data analysts are important members of a corporate finance team. They rely on a combination of technical skills, analytical skills and transferable skills to compile and communicate data and collaborate with their organizations to implement strategies that build profitability. If you’re interested in a career in financial analysis, there are several subfields to explore, including capital market analysis.

Organizations and corporates are using analytics and data to get insights into the market trends to make decisions that will have a better impact on their business. The organization involved in healthcare, financial services, technology, and marketing are now increasingly using big data for a lot of their key projects.

The financial services industry has adopted big data analytics in a wide manner and it has helped online traders to make great investment decisions that would generate consistent returns. With rapid changes in the stock market, investors have access to a lot of data.

Big data also lets investors use the data with complex mathematical formulas along with algorithmic trading. In the past, decisions were made on the basis of information on market trends and calculated risks. Computers are now used to feed in a large amount of data which plays a significant role in making online trading decisions.

The online trading landscape is making changes and seeing the use of increased use of algorithms and machine learning to compute big data to make decisions and speculation about the stock market.

Big Data influences online trading in 3 primary ways:

  1. Levels the playing field to stabilize online trade

Algorithmic trading is the current trend in the financial world and machine learning helps computers to analyze at a rapid speed. The real-time picture that big data analytics provides gives the potential to improve investment opportunities for individuals and trading firms.

  1. Estimation of outcomes and returns

Access to big data helps to mitigate probable risks in online trading and make precise predictions. Financial analytics helps to tie up principles that affect trends, pricing and price behaviour.

  1. Improves machine learning and delivers accurate predictions

Big data can be used in combination with machine learning and this helps in making a decision based on logic than estimates and guesses.  The data can be reviewed and applications can be developed to update information regularly for making accurate predictions.

In a nutshell, large financial firms to small-time investors can leverage big data to make positive changes to their investment decisions. Information is bought to the fingertips in an accessible format to execute trading decisions.

If you are a trader, you will benefit from a Big Data Analytics course to help you increase your chances of making decisions. It is highly beneficial for those involved in quant trading as it can be used extensively to identify patterns, and trends and predict the outcome of events. Volume, Velocity, and Variety are the pillars of Big Data that aid financial organizations and traders in deriving information for trading decisions.

Sandeep’s Review of Imarticus’ Data Science Course

Posted on by Imarticus Learning

We caught up with Sandeep, a recent graduate of the Post Graduate program in Analytics, for a quick chat to get his perspective on the program, the curriculum, Imarticus Learning’s placement process and more.
Tell us a little bit about yourself.

Sandeep: My name is Sandeep Singh. I recently completed my B.Sc. in Computer Science and was looking for an avenue to enhance my analytics skills and start my career.

Data Science Course in MumbaiI came across Imarticus’ data science course and, after thorough research, decided to enroll for it. I completed the course and have been placed at M Technologies through Imarticus.

How has your experience been with Imarticus Learning?
Sandeep: My experience with Imarticus Learning was super! The course focused on practical training with hands-on learning of various analytical tools and thorough practice with numerous datasets.

Looking back, I see the importance of actually applying Analytical tools and techniques to the projects I worked on because it gave me a running start when I began working.

What has changed since you joined Imarticus Learning?
Sandeep: Since the day I joined Imarticus my confidence has been boosted to a very high level. Through the practice of various analytical tools such as R, Python, SAS, Tableau, etc. I’ve come to believe in myself. My soft skills have also been elevated with the help of business communication workshops, mock interviews, and soft skill sessions throughout the course.

Would you recommend the program to someone else?
Sandeep: While researching various institutes, I came across some reviews that say Imarticus Learning is fake. Well, I wanted to see for myself and now that I have, I would definitely recommend Imarticus. If you’re looking for an institute, the first thing that comes to mind is the faculty and the learning material.

The faculty and staff are very cooperative and help you both inside and outside the classroom. The learning material is extensive and covers every aspect of data analytics. The best part is all of the lectures, notes, datasets, and quizzes are stored in an online Learning Management system and is available to students anytime, anywhere.

What do you like most about Imarticus?
The best thing about Imarticus Learning was the course content, the cooperative staff and the informative notes that are easily accessible. The resume building workshops and mock interviews definitely prepared me for the placement drives and I was able to crack the interview and land a job at M Technologies.

Looking to get started on your data science career, Speak with a counselor and get matched with the best course for you.