Data science market in India has grown exponentially in the last few years. Several industries are turning to big data techniques to create value for their business. As the competition in the market is at its all-time high, staying in the game as a leader is a new challenge.

Data analytics has always been a part of business concerns, but the latter is more proliferated and advanced. Let’s know how?

DATA SCIENCE AND DATA ANALYTICS

Industries like finance and banking have always used data analytics to know about risk and frauds that can happen, checking customer’s credit position and expenditure pattern. Data analytics used various quantitative and qualitative methods to see behavioral changes in data and patterns. It doesn’t use much mathematical and programming skills, but depend on soft skills.

Whereas, data science is the broader subject; it uses mathematical, statistical, programming, skills and on final stage rely on soft skills. Data analytics is a part of data science which starts from asking the correct questions and by using one’s curiosity to see what others cannot. That is the reason why data science is more in demand, so that other than just having a narrow lookout one should see future changes and be prepared for it.

GROWTH OF DATA SCIENCE

Analytics industry is earning revenues like never before; almost all industries are spending on analytics to increase one’s business value. Analytics industry is showing a growth rate of 33.5% CAGR. In no time data science will become the leading industry in revenue earning, innovation, and for providing career opportunities. Let’s discuss some leading sectors that are leveraging data science:

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  Social media and entertainment: Digital media are the new revolution, and the number of competitors is high. Major challenges include; knowing what customers want, how to expand distribution, collection, and knowing the right content. Applications include:
  
  1. Using recommendation systems to make the right content available.
  2. Creating the right kind of content.
  3. Checking the performance of content among the audience.
• 
  Retail and wholesale industry: This industry has many components like product demand and supply, customers, logistics, warehousing, advertising, pricing etc. All these data and information are used to make retailing business fruitful. Some of the uses are:
  
  1. To know about the market trends and fashion.
  2. Taking care of the inventory levels at all times.
  3. Knowing about the customer buying patterns and preferences.
  4. To keep a tab on frauds and defaults.
• 
  Banking and finance industry: One of the pioneers in data analysis, and still provides a major share of revenue to the analytics industry. This industry deals in various dimensions with two major variables, that is of money and customers. Some of the applications are:
  
  1. Big banks use it for the credit position of the customers.
  2. Several financial commissions use it for keeping a check on illegal activities in the financial market.
  3. Trading and investing firms use data to know about market moods and trends, so that they can mitigate risks of losing.
• 
  Manufacturing and natural resources industry: Both the industries deal with a lot of data which can help them in increasing their market performance and profit margins. It is used for:
  
  1. Natural resources industries use it for analyzing geospatial and geographical data.
  2. Manufacturing units use data science for maintaining inventory, optimizing production, and handling labor.

One common misconception among people about data science is that it is all about a single discipline. But the actual fact is that it is a blend of the various disciplines which are interconnected. Hence, it is interdisciplinary.

It is the science of using various scientific techniques to extract useful data in various forms from a large pool of information. Speaking of forms of data, the data may be structured or unstructured. This process is called data mining.

Is Data Science Worth The Hype?

Why not? According to Harvard Business Review of 2012, Data Science has been called the sexiest job anyone could get in the twenty first century. It has developed a lot in the recent years and there have been a significant increase in the number of jobs and vacancies in various local firms and multinational families due to the current increasing demand in data science in the information technology sector.

How is Data Science Related to Statistics?

It has not only brought a boon in the sector of information technology, but has also influenced the business sector to a large extent. There has been a noticeable rise in the job openings in the business sector as well. It is very closely linked to statistics. In fact, some data scientists have asserted that there is no difference between it and business statistics. According to them, they are the same. But, apart from that, there are some critics who have tried to belie the aforementioned assertion by stating that data science is just a redundant term that has arisen out of business analytics itself. But, the bottom line is that both data science and business analytics employ various scientific and non scientific techniques. Both of the things include using various scientific and non scientific methods to extract out and analyze data and use it in various contexts. Hence, it can be safely concluded that they are indeed very closely linked to one another.

Machine Learning

Machine learning is a very important aspect. Making a machine learn is something which comes from feeding the machine with data only. Hence, there are various aspects, but it is the data scientist who has to decide where his or her interest lies and where he or she should specialize. Machine learning is a very vast topic, yet it is just a fraction of data science. This can give one a clear idea as to how wide and vast the field of data science is. Also, machine learning is also divided into various subparts like artificial intelligence, also abbreviated as AI. It gives a computer an ability to communicate with the user and hence do the necessary task. One needs to be good at programming to be a good data scientist. For machine learning, programming in python is preferred mainly. But it is totally up to the user as to which programming language he or she wants to code in.

If you have a question in mind regarding the future of data science then you are definitely concerned with whether the techniques and tools such as Python, Hadoop or SAS will become outdated or whether investing in a data science course will be beneficial for your career in the long-run. But there is no need for worry. Businesses have only recently begun to realize the worth of their data and have just begun to make significant investment in these areas. So data science careers will be around for quite some time.

HISTORY OF DATA SCIENCE

The history of data as well as statistics is proof of the fact that the transformation of data into useful insights is something which has been happening for a long, long time.

The high-tech data-driven world has forced companies to develop cheaper and more reliable sources of data storage so as to store lots and lots of business data. The extraction of useful insights from this mass of data requires the skills and knowledge base of statistician and programmers. This combination of statistic skills and programming skills can be seen only in the DATA SCIENTIST. The job of data scientists is not only extracting useful insights but extends to designing new tools and techniques for processing and storage of data.

A SECURE AND LUCRATIVE CAREER AS A DATA SCIENTIST

The individuals with right mix of skills necessary for jobs as data scientists will have a challenging career. The job and salary prospects of DATA SCIENTISTS make it an even more worthwhile educational investment for new entrants and professionals in the near term. Data scientists will have a wonderful career in the future as well. The field of data science (machine learning in particular) is not going to disappear but rather outshine other fields. So the future of data science looks promising.

The market for data is regarded as highly competitive. Standing out from the crowd and having an edge over the competition are the two important traits to keep in mind when pursuing your career in the field of data science.

HOW CAN THIS BE ACHIEVED?

A lot has been discussed about the use and benefits of data. But now the question arises regarding how to accomplish training in this field. Professionals can advance their skills and knowledge base by taking classes in data as it is always said that upgrading one’s skillset (by way of training, education or any other mode) is the key to a successful career and a bright future. It is always beneficial to be in sync with the latest technology and trends.

THE FUTURE OF DATA SCIENCE IS DEFINITELY GOING TO BE BRIGHTER THAN EVER

But remember, getting into this field is not easy. You will need to have an in-depth and thorough knowledge of all the techniques and methodologies utilized in the field of data science.

Data Science, what is it?

With the growth of big data across all the different sectors of business and research, there is an increasing demand for Data Scientists. What this means is, there’s need to have more people working on the problems of getting unstructured data into a format which is helpful to the companies they work for. There are different kinds of jobs, but the crux of the job is organizing data and detecting patterns which can be utilized in the future for various purposes. This means people who want to go into it have to possess a clear understanding of business and mathematics along with a strong technological interest. It is constantly evolving, the problems of a decade ago are far behind us, and new ones emerge as technology develops.

What to do to become a Data Scientist

It is necessary to have strong analytical skills and be well-versed in mathematics and business. With a background in these subjects and a passion for technology, it will be a fascinating field to be in. There is a need to learn how to work in it, and many tricks and tips of the trade.

That is why it is good to attend a training program, either online or in a classroom, and once you become qualified to work in Data Science, there are many paths open to you. A training program will provide lectures, assignments and hands-on work which will help you learn everything you need about Data Science. These will be supplemented with videos you can revisit whenever you want to. There are lecturers with expertise in Data Science who will guide and advise you.

You will learn or receive a refresher on statistics, as well as learning a lot about programming languages and software related to data acquisition, hypothesis testing, regression analysis, machine learning, prediction models, and data visualization, among other things.

Once you’re qualified in Data Science

The training program will most likely have ties to companies which have a huge call for new employees to meet the demands of their data needs. Data Scientists are more in demand now than ever before. Getting from a training course to a placement in a reputed company will have you on your way to a bright future in the field. The only thing to make sure you have a good shot at is to work hard in your course, and keep refreshing the knowledge you gain. A curious and analytical mind-set will take you a long way. Data Science is rapidly growing and evolving, with new software and problems arising, working in this field will always be interesting.

A career in Data Science could mean working on machine learning algorithms, pure business analysis, general data analysis, or a combination of these all. The salary for Data Scientists in any of these positions is between 8-15 lakhs per annum, depending on the person’s abilities. There is no limit to the growth a person with Data Science training could experience in their careers.

Data science employs concepts and methods of data analysis, machine learning and statistics to derive an understanding and analysis of the data related phenomenon. Disciplines of Mathematics, Statistics, Computer science, and Information technology contributes to their theories and techniques in the establishment of the field of Data Science. The establishment of Data science as an independent term is a recent phenomenon. Earlier, it was used as an alternative for the term Computer Science. Interaction of data with certain processes and representation of data through various program forms the study area of computer science. The manipulation, storage, and communication of digital information require proficient use of algorithms. Computer Science facilitates the usage of these algorithms. A Computer Scientist learns to design software systems and gains in-depth knowledge of the theory of computation.

Knowledge of data helps you ask appropriate questions and derive insights from big data, it teaches you how to manipulate data sets and allows you to gain the skill of visualization of your own findings in a convincing manner. A well-designed course trains you about how to handle data science tools. The tools which build the foundation are Mathematical tools and computational tools. The in-depth understanding of these tools and proficiency in handling these tools helps one in suggesting data-driven solutions in the business.

Mathematical and applied are two aspects and to learn data science, one has to gain an understanding of both of these aspects. Probability, statistics, and machine learning come under the scope of Mathematical aspect while applied aspects help you gain knowledge of data science, languages which includes Python, MATLAB, JAVA, SQL. It also helps gives you an understanding of the usage of the specific toolkit. The applied aspects let you into the real data world. Training in a data science course gives you expertise in the collection of big data as well as its analysis and cleansing. This training assists you in executing analysis of big data on a large scale. It also trains you on how to communicate your findings in a compelling manner.

The term which shares a very close association with data science is Machine learning. Machine learning deals with algorithms to draw patterns out of data and to make predictions. For this purpose of making predictions and drawing out patterns, machine learning employed methods of data modeling. While making predictions, machine learning trains predictive models by making use of tagged data. The awareness of ground truth gives rise to observations which qualify themselves as tagged data. This task of making prediction includes training of models to enable them on how to prefigure the unknown data from tagged data. The training of models can be done by employing various methods. While some of these methods are simple, like regression, the others are complex, like neural nets. While discovering patterns out of data, machine learning tries to look for some patterns or search for some data associations in a situation where tagged data is absent. While there are more categories to machine learning, these two comprises of the core categories.

As homeschooling parents we give our children so many advantages as they move into the middle and upper grades and then to college. But, I believe there is one area where we can significantly improve the way we prepare them. That area is science.

Having taught science to several thousand homeschooling and college students over the past 25+ years, several things stand out to me. I’ve put together a list of 14 concrete steps we can take to better prepare our budding scientists.

Mistake #1 – Not starting to formally teach science early enough.

Start formally teaching science by the sixth grade. Students need the development that happens in those three years (sixth, seventh, and eighth grade) to prepare them for high school level science.

Mistake #2 – Generalizing the names for, and thus the way we teach, science. Call it Biology, Chemistry, Physics, etc. and not “General Science”, “Physical Science”, etc., even in the young grades. Doing this virtually eliminates the intimidation that comes with “Physics” etc in the high school years and clarifies what you’re teaching in the middle grades.

For example, at College Prep Science, rather than a homeschool year of “Physical Science,” we teach a semester of “Pre-Physics” and a semester of “Pre-Chemistry.” Rather than a homeschool year of “Life Science,” we teach a semester of “Pre-Biology” and a semester of “Pre-Anatomy and Physiology.”

Mistake #3 – Not doing enough testing.

Testing in the sciences prepares our students for the rigors of high school level science, college science, standardized testing, and assures that they are learning the material and that they are learning how to take tests. Of course it should be age appropriate but we should be testing.

Mistake #4 – Not doing timed tests.

I know that very few homeschooling families give their students timed tests and I think we are doing them a disservice. Gently beginning timed science tests in the middle grades gives students confidence, eliminates the anxiety associated with timed tests, and trains them to do well on standardized tests and on timed tests in college.

Start gently in the middle grades and slowly progress from there. For example, if you’re giving a student a 15 question test that you think will take them about 10 minutes to complete, tell them they have 25 minutes to take it. When they finish with plenty of time to spare it gives them confidence and relieves anxiety. The amount of extra time you give can be altered as they get older. Students actually do better on timed tests because they are focused on the test – knowing they have to work steadily. I always tell students, “If you are prepared and work steadily you will have plenty of time to finish this test.”

Mistake #5 – Teaching science year-round.

I know that many parents are proponents of year-round school (no summer break), but I believe it’s actually counter-productive. From experience with thousands of students I believe that students need to know they can work hard for a prescribed period of time and then have a total break from classes for two or three months.

Mistake #6 – Not starting the high school sciences early enough.

I know it’s easy to put off starting the high school sciences, but it’s important, especially if the students may be a college science major. Critical decisions should be made going into 8th grade. The critical factor is being ready for standardized testing and being able to fit in the needed sciences in the high school years. High school Biology should be taken in the 9th grade for most students and in the 8th grade for capable students who will likely be science majors.

Mistake #7 – Not beginning to take the ACT early enough.

Success on this standardized test is critical for college admissions and plays a direct role in how much financial aid a student will receive. Taking these tests twice per year beginning in 8th grade gives students experience and confidence which enables them to do well when they take this test for the final time in the spring of 11th grade. See my separate article on this topic.

Mistake #8 – Not specifically preparing to take the “Science Reasoning” section of the ACT. Homeschooled students score lower on this section of the ACT than on any other section. This is a section of the ACT that can be quite intimidating but can be mastered with preparation. It’s especially important if you are planning on a science major in college. See my separate article on this topic.

Mistake #9 – Not training students to meet deadlines.

As homeschoolers in general, this is a critical weakness and I think it’s even more important that we address this in the sciences. Beginning in the middle school grades, give your students firm deadlines that need to be met for assignments, tests, papers, etc. and stick to them. Besides being good training for academics, it’s just good life training too.

Mistake #10 – Not training students to write good lab reports.

As a college professor, I saw the pain of students who came in as science majors without good lab report writing skills and experience. Students get better at this with experience – there’s no substitute for that. Lab reports are simply the written summary of the scientific method. It takes lots of practice to develop the skill needed to do well on these.

Mistake #11 – Not creating a lab manual for every science class.

A lab manual is a collection of observations, data collection, and lab reports from a class. This gives students one place to neatly keep all of this information and gives them a sense of accomplishment. It’s impressive to have them lined-up on a shelf from all of their science classes. It’s also required by some states or umbrella groups for homeschoolers and some colleges want to see lab manuals as evidence of labs being completed.

Mistake #12 – Not encouraging exploration.

Encourage and give your students opportunities to be curious about God’s creation around them. Then, encourage them to experiment to answer questions about anything. This doesn’t have to be ground-breaking research but just simple things. Then, encourage them to write things down in a notebook. That may be the beginnings of a budding scientist at work.

Mistake #13 – Being squeamish on Creation.

Despite what you may hear in the media and elsewhere, God wrote the book on science. We need to boldly teach our students about God’s creation. Science and the world around us support biblical creation.

Mistake #14 – Not using graphing extensively.

Graphing, when done regularly through the middle and high school grades has a unique ability to develop critical thinking skills in students that not only benefit them in math, science, and academics in general, but also in life! We encourage families to have students construct one graph daily as part of their homeschooling day. They can graph anything. Let them run with it and you will be surprised at how creative they are. The resulting skills can be very, very beneficial.

What is Data Science?

The wide application of Information Technology and Computer Science has given rise to so many new fields in the corporate sector which have enormous potentials and possibilities. One of the fastest growing careers among them is Data Science, which has become extremely popular among youth because of its exciting nature of work and new newness. Professionals who do this job are known as Data Scientists.

The demand for Data Scientists is increasing all over the world, and the world’s reputed organizations have listed this profession as the best profession of the 21st century. And the fact that there aren’t enough Data Scientists currently implies great opportunities for the newcomers in this field.

What Is Data Science All About?

We live in an age where we are surrounded by data. Communicating with all this data is a challenging task. Earlier, industries relied on simple tools such as BI for Data Mining, but with the advent of technical statistics and computer science, this has evolved to be known as Data Science. The introduction of Big Data and its relation to Information Technology led to its grand scale rise.

With Data Science, we can learn the hidden information in the data, and by applying analytics, we can use this information to solve business problems, foresee future trends and understand certain patterns which would be very difficult to be performed just by applying human intellect. But only mining some diverse set of data is not enough. The success depends on building programs based on the data which enhances the industrial results, and that’s what Data Scientists are expert in.

It is actually a platform where Business, Computer Science and Statistics come together. The job of a Data Scientist includes:

1. Organizing: This refers to the collection of data through open source software frameworks such as Hadoop and SAS.
2. Modeling: This is where a Data Scientist transforms, integrates and refines the data in order to understand it and create statistical models which can be applied to solve the problem at hand.
3. Delivering: After a model is constructed, the Data Scientist explains the model to the customer and other members.

Benefits of Applying Data Science in Industry

All major companies have reported excellent improvement in their business results by applying Data Science to exploit their user’s data and understand how to improve their products. The prime reason why companies like Google, Amazon and Apple are so ahead of their competitors is that they’re making excellent use of Data Science to track each and every user and draw inferences from their behavior and shopping pattern. This can be seen easily in the preciseness of Google searches, Facebook’s newsfeed recommendation and Amazon’s products suggestions. But this is not the end because Data Science is also creating milestones in industries like Medical Science, Banking and Finance, Online Education, Security Systems, Aviation Industry… and this list can go on and on.

Your Way to Become a Data Scientist

Data Scientists are the professionals who have a good knowledge of programming, statistics, mathematics and computers. They can deal with any type of data and process it to make it meaningful. The online course is designed to prepare students experts in all the concepts and tools which are employed in Data Science.

IMPORTANCE:

Data scientists are people who always have an exquisite mindset and want to develop stuff. There is definitely a lack of data scientists in the block with all the essential skills which makes it difficult for companies to meet the demand and hence due to this reason data science course has attracted people. Data Science gives you an opportunity to work with big brands due to their capability to make decisions regarding product features, pricing and changes. This is one of the safest careers to pursue right now due to the revolution of data science in the tech world. Whichever industry that you are working in, always has different unattended data surrounding it waiting to be explored and give meaning to.

JOB PROSPECTS:

Established as well as startup companies resort to data scientists because of its rising popularity now. This has led to huge job opportunities and data scientists can apply to various jobs which can be as a Statistician as you get well acquainted in math, Software programming analyst, Data engineer, Quality analyst, Spatial data scientist, etc and many others. But the important point is to understand the domain that you are interested in as the skill set of data science is huge with knowledge about many fields included.

DESCRIPTION ABOUT TASKS OF DATA SCIENTIST:

Data science is basically a detailed description or prediction for the betterment of future, wherein you apply these three major skills in a systematic manner which are understanding of mathematics, statistics and algorithms, programming and hacking as well as develop communication skills which are necessary for business. The process of data science is as follows: first you need to collect the correct raw data which are required for problem solving, but the data that you acquire cannot be used as you need to process and clean the data to remove all the corrupt records which is known as data wrangling. The next important step is to analyze the data to granular levels and identify the trends and patterns. Then perform in depth analysis of the data by all the techniques such as machine learning, statistical models, etc to make the data useful from extreme level. And finally the most important step is to be able to communicate your results to the stakeholders in a way that is easy for anyone to understand.

CHALLENGES FACED BY DATA SCIENTIST:

Every good thing always has a hindrance associated with it; data science too has the same problem. You should be able to explain your research and findings to non technical audiences who have no idea regarding the concepts. Getting equipped enough to handle raw data and be able to perform all the nitty-gritty stuff like cleaning, extracting, processing etc. Having a specific domain expertise is also tough for some also answering questions and doubts of the audience is of prime importance which sometimes people are not able to do. Privacy and security are also major concern topics for data scientist.

What is Data Science?

The data is all around us and is running on a constantly increasing path as the world is interacting more and more with the internet. The industries have now realized the tremendous power behind data and are figuring out how it can change not only the way of doing business but also the way we understand and experience things. Data Science refers to the science of decoding the information from a particular set of data. In general, Data Scientists collect raw data, process it into datasets, and then use it to construct statistical models and machine learning models. To do this, they need the following:

1. Data collection framework such as Hadoop, and programming languages such as SAS to write the sequels and queries.
2. Tools for data modeling such as python, R, Excel, Minitab etc.
3. Machine learning algorithms such as Regression, Clustering, Decision-tree, Support Vector Mechanics etc.

Components of a Data Science Project

• Studying Concepts: The first step involves meeting with the stakeholders and asking many questions in order to figure out the problems, available resources, involved conditions, budget, deadlines etc.
• Data Exploring: Many times the data can be ambiguous, incomplete, redundant, wrong or unreadable. To deal with these situations, Data Scientists explore the data by looking at samples and trying out ways to fill the blanks or remove the redundancies. This step may involve techniques like Data transformation, Data Integration, Data cleansing, Data reducing etc.
• Model Planning: The model can be any type of model such as statistical or machine learning model. The selection varies from one Data Scientist to another, and also according to the problem at hand. If it is a regression model, then one can choose regression algorithms, or if it is about classifying, then classification algorithms such as Decision-tree can produce the desired result.

Model Building refers to training the model so that it can be deployed where it’s needed. This step is mainly carried by Python packages like Numpy, pandas, etc. This is an iterative step i.e. a Data Scientist has to train the model multiple times.

• Communication: Next step is communicating the results to appropriate stakeholders. It is done by preparing easy charts and graphs showing the discovery and proposed solutions to the problem. Tools like Tableau and Power BI are extremely useful for this step.
• Testing and operating: If the proposed model is accepted, then it is led through some pre-production tests such as A/B testing, which is about using, say 80% of the model for training, and rest for checking the statistics of how well it works. Once the model has passed the tests, it is deployed in the production environment.

What Should You Do In Order To Become a Data Scientist?

Data Science is the fastest growing career of the 21st century. The job is challenging and allows the users to use their creativity to the fullest. Industries are in great need of skilled professionals to work on the data they are generating. And that is why this course has been designed to prepare students to lead the world in Data Science. Detailed training by reputed faculties, multiple assessments, live projects, webinars and many other facilities are available to shape students according to the industrial need.

The science of deliberate creation is about becoming more conscious. By becoming aware of your own preconceived ideas, beliefs and attitudes, you can see what is possible, and what isn’t.

Nothing is possible without belief. Without belief in an idea, you won’t take action and nothing can be achieved. But most people put the brakes on their own lives because their belief doesn’t allow the possibility of great things.

People can live great lives only if they believe they can. Otherwise, you wouldn’t attempt to improve things. Why bother if you don’t think you can do it?

This is the beginning of the science of deliberate creation and it starts with your thinking. Thoughts are perhaps the smallest parts of your creation process. But like a drip of water in a cave, large formations can form over time.

Stalactites and stalagmites are large formations in a cave which form because of the drippings of water depositing material over a long period of time. I like to think these are like your beliefs and ideas forming in the mind over long periods of thinking in a certain way.

By taking control over the thoughts which you allow to remain in the conscious mind, you can influence your subconscious mind. This in turn is capable of creating beliefs and attitudes and attracting circumstances and events into your life.

The subconscious mind is much more powerful than the conscious mind and the thoughts can be considered the ‘seeds’ of the subconscious belief system.

If you’ve ever worked really hard at something only to self-sabotage it later on, this is an example of the subconscious mind working against your conscious wishes. This can be for many reasons but often it’s because your conscious and subconscious are not working together as a “team”.

Other considerations of the science of conscious creation are your words, visualisations and plans for the future. Becoming more conscious of your thoughts, words and actions is a good place to start. Many people self sabotage their best intentions with their words, proclaiming that they are “not good enough”, “don’t deserve something” or are not up to the job. Unless you can consolidate what you want to attain, with how to get it and align the two with your self-image, you’ll find ways to un-do your work.

This is often just the sub-conscious mind delivering what you intended anyway. Perhaps you didn’t realise it, but you were sending contrary intentions to your subconscious mind in the form of words, thoughts or intentions.

Your self-image has a large part to play in conscious creation. Unless you see yourself as someone worthy of success and happiness, you’ll keep hitting a brick wall and falling back to your previous view of yourself and model of the world.

The old self-image keeps us safe inside our comfort zone. That’s the positive pay-off. But there’s a lot of downside to maintaining a consistent view of the world and how we fit into it.

If we doggedly stick to our preconceived ideas in all circumstances, it can eventually bite us on the ass! Particularly if we don’t grow and change as our circumstances and needs change.

Becoming more conscious of our thoughts starts by being aware enough to know that we are not our thoughts. The thoughts we think can easily overtake our idea of ourselves. If we start to see ourselves as the thoughts themselves, we lose control by identifying with the thoughts as “us”.

One way to detach from our thinking processes is through meditation. Anyone can learn to meditate simply by sitting still and quietly watching their thoughts come and go without attachment. The more you practice, the more you will experience the gap between the thoughts and be able to choose your thoughts more consciously.

Being aware of what you say is the next thing to take control of. Much of people’s speech in general runs on complete autopilot. When we become conscious, we can align our speaking with our thinking. We can become more conscious of the power of our words to carve out our world.

Visualisation is another tool for the conscious creator to use in ‘sculpting’ their lives more consciously. We’ve all experienced a daydream. A daydream is a nice relaxing fantasy where we can let our mind wander and think about a nice situation or circumstance of our choosing.

But in our busy and stressful lives, we can start creating arguments and conflicts with our visualisations inadvertently. By trying to control our circumstances we can often focus on trying to push against problems and adversity rather than focusing on joy, happiness and contentment. When we try too hard to push things away by struggling with them, we are focusing on the very things we don’t want.

The more attention we give them, the more they seem to wrap themselves around our lives. What you focus on grows in your life. Noticing this is the first step to becoming a more conscious creator. Then you can stop focusing on those things which you don’t want, and turn your attention to more positive things, circumstances and situations.